Dataset Information

Modeling Pediatric Body Mass Index and Neighborhood Environment at Different Spatial Scales.

ABSTRACT: In public health research, it has been well established that geographic location plays an important role in influencing health outcomes. In recent years, there has been an increased emphasis on the impact of neighborhood or contextual factors as potential risk factors for childhood obesity. Some neighborhood factors relevant to childhood obesity include access to food sources, access to recreational facilities, neighborhood safety, and socioeconomic status (SES) variables. It is common for neighborhood or area-level variables to be available at multiple spatial scales (SS) or geographic units, such as the census block group and census tract, and selection of the spatial scale for area-level variables can be considered as a model selection problem. In this paper, we model the variation in body mass index (BMI) in a study of pediatric patients of the Virginia Commonwealth University (VCU) Medical Center, while considering the selection of spatial scale for a set of neighborhood-level variables available at multiple spatial scales using four recently proposed spatial scale selection algorithms: SS forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso. For pediatric BMI, we found evidence of significant positive associations with visit age and black race at the individual level, percent Hispanic white at the census block group level, percent Hispanic black at the census tract level, and percent vacant housing at the census tract level. We also found significant negative associations with population density at the census tract level, median household income at the census tract level, percent renter at the census tract level, and exercise equipment expenditures at the census block group level. The SS algorithms selected covariates at different spatial scales, producing better goodness-of-fit in comparison to traditional models, where all area-level covariates were modeled at the same scale. These findings underscore the importance of considering spatial scale when performing model selection.

SUBMITTER: Grant LP

PROVIDER: S-EPMC5877018 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Modeling Pediatric Body Mass Index and Neighborhood Environment at Different Spatial Scales.

Grant Lauren P LP Gennings Chris C Wickham Edmond P EP Chapman Derek D Sun Shumei S Wheeler David C DC

International journal of environmental research and public health 20180308 3

In public health research, it has been well established that geographic location plays an important role in influencing health outcomes. In recent years, there has been an increased emphasis on the impact of neighborhood or contextual factors as potential risk factors for childhood obesity. Some neighborhood factors relevant to childhood obesity include access to food sources, access to recreational facilities, neighborhood safety, and socioeconomic status (SES) variables. It is common for neigh ...[more]

PMID: 29518029

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Project description:To investigate the association between the neighborhood food environment and body mass index (BMI) among Chinese older adults. A multi-stage stratified random sampling method was used to recruit participants from 12 communities in Beijing, China, in 2019. Participants (n = 1764, 1034 women) in this study were older adults aged 65 to 80. We collected the participants' basic information, measured their height and weight, and calculated their BMI. Neighborhood food environments were measured by the density of and proximity to different food outlets using the Baidu Map Application Programming Interface. Adjusted multiple linear regression was performed to estimate the association between the food environment and BMI. Participants had a mean age of 69.7 ± 4.32 years old and an average BMI of 26.3 ± 3.50 kg/m2. Among the three types of stores, convenience stores had the easiest access, followed by greengrocers and supermarkets. Sit-down restaurants had the best access among different restaurants, followed by Chinese fast-food restaurants, and western fast-food restaurants had the worst access. Easier access to greengrocers (β = 0.281, p < 0.001) and sit-down restaurants (β = 0.304, p < 0.001) was associated with higher BMI in the 250 m buffer zone. More supermarkets were associated with higher BMI in the 500 m buffer zone (β = 0.593, p < 0.001). Access to convenience stores was positively associated with BMI in the 800 m buffer zone (β = 0.057, p < 0.001). Better access to Chinese fast-food restaurants was associated with higher BMI (β = 0.071, p = 0.001), and better access to western fast- food restaurants was associated with lower BMI (β = -0.400, p < 0.001) in the 1000 m buffer zone. There was a negative association between the nearest distance to greengrocers and BMI (β = -0.004, p < 0.001). Although we found some significant associations between the neighborhood food environment and obesity, the current results are not strong enough to draw specific conclusions. Policymakers will need to rely on more evidence to derive concrete policy recommendations.

Project description:BACKGROUND:Perceived neighborhood characteristics are linked to obesity, however, the mechanisms linking these two factors remain unknown. PURPOSE:This study aimed to examine associations between perceived neighborhood characteristics and body mass index (BMI), establish whether indirect pathways through psychological distress and inflammation are important, and determine whether these associations vary by race/ethnicity. METHODS:Participants were 1,112 adults enrolled in the Texas City Stress and Health Study. Perceived neighborhood characteristics were measured using the Perceived Neighborhood Scale. Psychological distress was measured with the Center for Epidemiological Studies Depression Scale, Perceived Stress Scale and mental health subscale of the Short Form Health Survey-36. Markers of inflammation included C-reactive protein, interleukin-6, and tumor necrosis factor receptor-1. Associations were examined with Structural Equation Modeling. RESULTS:A model linking neighborhood characteristics with BMI through direct and indirect (i.e., psychological distress and inflammation) paths demonstrated good fit with the data. Less favorable perceived neighborhood characteristics were associated with greater psychological distress (B = -0.87, ? = -0.31, p < .001) and inflammation (B = -0.02, ? = -0.10, p = .035). Psychological distress and inflammation were also significantly associated with BMI (Bdistress = 0.06, ? = 0.08, p = .006; Binflammation = 4.65, ? = 0.41, p < .001). Indirect paths from neighborhood characteristics to BMI via psychological distress (B = -0.05, ? = -0.03, p = .004) and inflammation (B = -0.08, ? = -0.04, p = .045) were significant. In multiple group analysis, a model with parameters constrained equal across race/ethnicity showed adequate fit suggesting associations were comparable across groups. CONCLUSION:Our study extends the literature by demonstrating the importance of neighborhood perceptions as correlates of BMI across race/ethnicity, and highlights the role of psychological and physiological pathways.

Dataset Information

Modeling Pediatric Body Mass Index and Neighborhood Environment at Different Spatial Scales.

Publications

Modeling Pediatric Body Mass Index and Neighborhood Environment at Different Spatial Scales.

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