Multilevel built environment features and individual odds of overweight and obesity in Utah

Xu, Yanqing; Wen, Ming; Wang, Fahui

doi:10.1016/j.apgeog.2014.10.006

Cited by 58 publications

(51 citation statements)

References 36 publications

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“…It may also work in a reverse direction as some minorities (e.g., Blacks and Hispanics) are reported to experience higher obesity rates and thus a neighborhood with above-average representations of these minorities could have a relatively high heterogeneity index. The index is used in several studies of community environment and obesity risk (Wen and Kowaleski-Jones 2012, Wang, Wen and Xu 2013, Xu, Wen and Wang 2015). …”

Section: Data Sources and Variable Definitionsmentioning

confidence: 99%

“…There are other measures for food environment (Mehta and Chang 2008; Chi et al 2013; Xu and Wang 2015). As suggested by Xu, Wen and Wang (2015), the fast food restaurant ratio is an adequate measure at the county level. While walk score and food environment are used in many studies for obesity risk, our attempt is the first at a national scale.…”

Section: Data Sources and Variable Definitionsmentioning

confidence: 99%

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Built environment and obesity by urbanicity in the U.S.

Wang

2015

Health & Place

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Based on the data from the Behavioral Risk Factor Surveillance System 2012, this study examines the association of neighborhood built environments with individual physical inactivity and obesity in the U.S. Multilevel modeling is used to control for the effects of individual socio-demographic characteristics. Neighborhood variables include built environment, poverty level and urbanicity at the county level. Among the built environment variables, a poorer street connectivity and a more prominent presence of fast-food restaurants are associated with a higher obesity risk (especially for areas of certain urbanicity levels). Analysis of data subsets divided by areas of different urbanicity levels and by gender reveals the variability of effects of independent variables, more so for the neighborhood variables than individual variables. This implies that some obesity risk factors are geographically specific and vary between men and women. The results lend support to the role of built environment in influencing people’s health behavior and outcome, and promote public policies that need to be geographically adaptable and sensitive to the diversity of demographic groups.

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Section: Data Sources and Variable Definitionsmentioning

confidence: 99%

Section: Data Sources and Variable Definitionsmentioning

confidence: 99%

Built environment and obesity by urbanicity in the U.S.

Wang

2015

Health & Place

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“…Recent studies found inverse relationships between urban parks and natural amenities and weight status such that locations with greater access experienced lower body mass indices (BMI) (Pitts et al, 2013, Rundle et al, 2013, Stark et al, 2014, Boncinelli et al, 2015, Xu et al, 2015). Several studies, however, found no or a positive relationship between greenspace exposure and weight status (Rundle et al, 2013, Lachowycz and Jones, 2011).…”

Section: Introductionmentioning

confidence: 99%

Varying age-gender associations between body mass index and urban greenspace

Sander

Ghosh

Hodson

2017

Urban Forestry & Urban Greening

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Urban greenspace benefits urbanites in numerous ways ranging from regulating flooding, air quality, and local climate to providing opportunities for exercise and relaxation. These benefits may influence human health. Greenspace, for example, may facilitate exercise, thereby helping to reduce body mass index (BMI) and combat obesity, a current epidemic of great public health concern. Little evidence exists to support this assertion, however, and we lack a full understanding of the mechanisms whereby this relationship operates, the populations for whom greenspace is linked to weight status, and the aspects of urban greenspace that are linked to weight status. This study seeks to identify relationships among the composition and arrangement of greenspace and BMI for different populations using regression models for eight age and gender groups in Cleveland, Ohio, US. We find that several greenspace variables are related to BMI for women under 65 years and males under 51 years, but not for older groups, and that the aspects and types of greenspace that are significantly related to BMI vary among groups. Relationships between greenspace attributes and BMI are generally stronger for female groups and for younger groups. Providing access to greenspace with particular attributes such as greenspaces with water, canopy cover, or connected greenspaces could support a healthy weight status for some populations, but these attributes are not consistent across age and gender groups. These results could help to inform policy aimed at designing urban greenspace to benefit the health of different population subgroups.

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“…When geocoding of individual-level data is limited to pre-defined area units, one needs to explore which level of neighbourhood is most relevant and assess whether such an effect is supported by underlying behaviours. In a study on the associations between neighbourhood built environments and individual odds of overweight and obesity in Utah, Xu, Wen, and Wang (2015) employ the measures of neighbourhood variables at two levels. They found that distance to parks at the ZIP code area level and food environment (fast food ratio) at the county level are significant factors linked to risks of overweight and obesity.…”

Section: From Area-based To Individualized Neighbourhood Effectsmentioning

confidence: 99%

Why public health needs GIS: a methodological overview

Wang

2019

Annals of GIS

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142

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The short paper provides an overview on how geographic issues have become increasingly relevant to public health research and policy, particularly through the lens of geographic information systems (GIS). It covers six themes with an emphasis on methodological issues. (1) Our health-related behaviour varies across geographic settings, so should public health policy. (2) Facilities (supply) and patients (demand) in a health-care market interact with each other across geopolitical borders, and measures of healthcare accessibility need to capture that. (3) Our health outcome is the result of joint effects of individual attributes and neighbourhood characteristics, and an adequate definition of neighbourhood is critical for assessing neighbourhood effect. (4) Disease rates in areas of small population are unreliable, and one effective way to mitigate the problem is to construct a larger, internally homogenous and comparable area unit. (5) Defining a scientific geographic unit for health-care market is critical for researchers, practitioners, and policy makers to evaluate health-care delivery, and GIS enables us to define the unit (e.g. primary care service areas, hospital service areas, and cancer service areas) automatically, efficiently and optimally. (6) Aside from various optimization objectives around 'efficiency', it is as important to plan the location and allocation of health-care resources towards maximum equality in health-care access. Case studies are cited to illustrate each theme.

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Multilevel built environment features and individual odds of overweight and obesity in Utah

Cited by 58 publications

References 36 publications

Built environment and obesity by urbanicity in the U.S.

Built environment and obesity by urbanicity in the U.S.

Varying age-gender associations between body mass index and urban greenspace

Why public health needs GIS: a methodological overview

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