Exploring the forest instead of the trees: An innovative method for defining obesogenic and obesoprotective environments

Nau, Claudia; Ellis, Hugh; Huang, Hongtai; Schwartz, Brian S.; Hirsch, Annemarie G.; Bailey‐Davis, Lisa; Kress, Amii M.; Pollak, Jonathan; Glass, Thomas A.

doi:10.1016/j.healthplace.2015.08.002

Cited by 30 publications

(41 citation statements)

References 69 publications

(77 reference statements)

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“…Considering the degree of overall healthiness of the food mainly provided in each type of food outlets, we hypothesized that decreased exposure to supermarket, full‐service restaurant, health/dietetic food store, fruit/vegetable market, and beverage store was associated with higher weight status, while decreased exposure to convenience store, fast‐food restaurant, retail bakery, dairy‐product store, candy store, and meat/fish market was associated with lower weight status …”

Section: Methodsmentioning

confidence: 99%

“…36 Considering the degree of overall healthiness of the food mainly provided in each type of food outlets, we hypothesized that decreased exposure to supermarket, full-service restaurant, health/dietetic food store, fruit/vegetable market, and beverage store was associated with higher weight status, while decreased exposure to convenience store, fast-food restaurant, retail bakery, dairy-product store, candy store, and meat/fish market was associated with lower weight status. [38][39][40] A widely accepted hypothesis that healthier weight status often relates to a greater land use mix 41 was adapted to this study to examine the association between the food outlet mix (ie, the heterogeneity of the FE) and weight status. An entropy score 41 was used to describe the food outlet mix within a given ZIP code and defined as -…”

Section: Outcome Variablesmentioning

confidence: 99%

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Improvement in food environments may help prevent childhood obesity: Evidence from a 9‐year cohort study

et al. 2019

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Summary Background Effects of food environments (FEs) on childhood obesity are mixed. Objectives To examine the association of residential FEs with childhood obesity and variation of the association across gender and urbanicity. Methods We used the US Early Childhood Longitudinal Study—Kindergarten Cohort data, with 9440 kindergarteners followed up from 1998 to 2007. The Dun and Bradstreet commercial datasets in 1998 and 2007 were used to construct 12 FE measures of children, ie, changes in the food outlet mix and density of supermarkets, convenience stores, full‐service restaurants, fast‐food restaurants, retail bakery, dairy‐product stores, health/dietetic food stores, confectionery stores, fruit/vegetable markets, meat/fish markets, and beverage stores. Two‐level mixed‐effect and cluster robust logistic regression models were fitted to examine associations. Results Decreased exposures to full‐service restaurants, retail bakeries, fruit/vegetable markets, and beverage stores were generally obesogenic, while decreased exposure to dairy‐product stores was generally obesoprotective; the magnitude and statistical significance of these associations varied by gender and urbanicity of residence. Higher obesity risk was associated with increased exposure to full‐service restaurants among girls, and with decreased exposures to fruit/vegetable markets in urban children, to beverage stores in suburban children, and to health/dietetic food stores in rural children. Mixed findings existed between genders on the associations of fruit/vegetable markets with child weight status. Conclusion In the United States, exposure to different FEs seemed to lead to different childhood obesity risks during 1998 to 2007; the association varied across gender and urbanicity. This study has important implications for future urban design and community‐based interventions in fighting the obesity epidemic.

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Section: Methodsmentioning

confidence: 99%

Section: Outcome Variablesmentioning

confidence: 99%

Improvement in food environments may help prevent childhood obesity: Evidence from a 9‐year cohort study

et al. 2019

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“…Numerous methods build classifiers, such as support vector machines, decision trees, and rulesets [ 21 ]. As detailed in the Introduction, our study uses decision trees, which are a commonly used approach [ 6 - 9 , 11 - 13 ] that provides a usable visual tool ( Figure 1 ) to support decision-making activities such as triage.…”

Section: Methodsmentioning

confidence: 99%

“…There are many algorithms to choose from when performing classification. Decision trees in particular have proven to be a popular approach [ 6 - 9 , 11 - 13 ] for at least 2 reasons. First, they can then be used as a visual tool: instead of being a black-box model (such as a deep neural network or a support vector machine), they clearly articulate the rules that transform the description of a new participant’s case into an outcome ( Figure 1 ).…”

Section: Introductionmentioning

confidence: 99%

Accurately Inferring Compliance to Five Major Food Guidelines Through Simplified Surveys: Applying Data Mining to the UK National Diet and Nutrition Survey

Rosso¹,

Giabbanelli²

2018

JMIR Public Health Surveill

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BackgroundNational surveys in public health nutrition commonly record the weight of every food consumed by an individual. However, if the goal is to identify whether individuals are in compliance with the 5 main national nutritional guidelines (sodium, saturated fats, sugars, fruit and vegetables, and fats), much less information may be needed. A previous study showed that tracking only 2.89% of all foods (113/3911) was sufficient to accurately identify compliance. Further reducing the data needs could lower participation burden, thus decreasing the costs for monitoring national compliance with key guidelines.ObjectiveThis study aimed to assess whether national public health nutrition surveys can be further simplified by only recording whether a food was consumed, rather than having to weigh it.MethodsOur dataset came from a generalized sample of inhabitants in the United Kingdom, more specifically from the National Diet and Nutrition Survey 2008-2012. After simplifying food consumptions to a binary value (1 if an individual consumed a food and 0 otherwise), we built and optimized decision trees to find whether the foods could accurately predict compliance with the major 5 nutritional guidelines.ResultsWhen using decision trees of a similar size to previous studies (ie, involving as many foods), we were able to correctly infer compliance for the 5 guidelines with an average accuracy of 80.1%. This is an average increase of 2.5 percentage points over a previous study, showing that further simplifying the surveys can actually yield more robust estimates. When we allowed the new decision trees to use slightly more foods than in previous studies, we were able to optimize the performance with an average increase of 3.1 percentage points.ConclusionsAlthough one may expect a further simplification of surveys to decrease accuracy, our study found that public health dietary surveys can be simplified (from accurately weighing items to simply checking whether they were consumed) while improving accuracy. One possibility is that the simplification reduced noise and made it easier for patterns to emerge. Using simplified surveys will allow to monitor public health nutrition in a more cost-effective manner and possibly decrease the number of errors as participation burden is reduced.

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“…First, we quantified EJ indicators (poverty and race/ethnicity measures) for census tracts across the United States and joined them to ADD estimates of chemicals and chemical mixtures, using American Community Survey (ACS) data, the 2005 National-Scale Air Toxics Assessment (NATA), and the EPA Exposure Factors Handbook [ 25 ]. Poverty and race/ethnicity are indicative of many factors that could influence various components of the ADD model, such as exposure frequency and duration (for exposure factor), emission source prevalence (concentration), and public health (intake rate and body weight), all of which are concerns in EJ neighborhoods [ 9 , 21 , 41 , 45 , 46 , 47 , 48 ]. Second, based on a simulation of communities with different EJ-related scenarios, we estimated ADD levels for communities exposed to lead via soil/dust ingestion to show the utility of using EJ-ADD to estimate average daily dose with consideration of EJ indicators on a local scale.…”

Section: Introductionmentioning

confidence: 99%

Connecting the Dots: Linking Environmental Justice Indicators to Daily Dose Model Estimates

Huang

Barzyk

2016

IJERPH

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Many different quantitative techniques have been developed to either assess Environmental Justice (EJ) issues or estimate exposure and dose for risk assessment. However, very few approaches have been applied to link EJ factors to exposure dose estimate and identify potential impacts of EJ factors on dose-related variables. The purpose of this study is to identify quantitative approaches that incorporate conventional risk assessment (RA) dose modeling and cumulative risk assessment (CRA) considerations of disproportionate environmental exposure. We apply the Average Daily Dose (ADD) model, which has been commonly used in RA, to better understand impacts of EJ indicators upon exposure dose estimates and dose-related variables, termed the Environmental-Justice-Average-Daily-Dose (EJ-ADD) approach. On the U.S. nationwide census tract-level, we defined and quantified two EJ indicators (poverty and race/ethnicity) using an EJ scoring method to examine their relation to census tract-level multi-chemical exposure dose estimates. Pollutant doses for each tract were calculated using the ADD model, and EJ scores were assigned to each tract based on poverty- or race-related population percentages. Single- and multiple-chemical ADD values were matched to the tract-level EJ scores to analyze disproportionate dose relationships and contributing EJ factors. We found that when both EJ indicators were examined simultaneously, ADD for all pollutants generally increased with larger EJ scores. To demonstrate the utility of using EJ-ADD on the local scale, we approximated ADD levels of lead via soil/dust ingestion for simulated communities with different EJ-related scenarios. The local-level simulation indicates a substantial difference in exposure-dose levels between wealthy and EJ communities. The application of the EJ-ADD approach can link EJ factors to exposure dose estimate and identify potential EJ impacts on dose-related variables.

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Exploring the forest instead of the trees: An innovative method for defining obesogenic and obesoprotective environments

Cited by 30 publications

References 69 publications

Improvement in food environments may help prevent childhood obesity: Evidence from a 9‐year cohort study

Improvement in food environments may help prevent childhood obesity: Evidence from a 9‐year cohort study

Accurately Inferring Compliance to Five Major Food Guidelines Through Simplified Surveys: Applying Data Mining to the UK National Diet and Nutrition Survey

Connecting the Dots: Linking Environmental Justice Indicators to Daily Dose Model Estimates

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