Dataset Information

Optimizing Scoring and Sampling Methods for Assessing Built Neighborhood Environment Quality in Residential Areas.

ABSTRACT: Optimization of existing measurement tools is necessary to explore links between aspects of the neighborhood built environment and health behaviors or outcomes. We evaluate a scoring method for virtual neighborhood audits utilizing the Active Neighborhood Checklist (the Checklist), a neighborhood audit measure, and assess street segment representativeness in low-income neighborhoods. Eighty-two home neighborhoods of Washington, D.C. Cardiovascular Health/Needs Assessment (NCT01927783) participants were audited using Google Street View imagery and the Checklist (five sections with 89 total questions). Twelve street segments per home address were assessed for (1) Land-Use Type; (2) Public Transportation Availability; (3) Street Characteristics; (4) Environment Quality and (5) Sidewalks/Walking/Biking features. Checklist items were scored 0-2 points/question. A combinations algorithm was developed to assess street segments' representativeness. Spearman correlations were calculated between built environment quality scores and Walk Score®, a validated neighborhood walkability measure. Street segment quality scores ranged 10-47 (Mean = 29.4 ± 6.9) and overall neighborhood quality scores, 172-475 (Mean = 352.3 ± 63.6). Walk scores® ranged 0-91 (Mean = 46.7 ± 26.3). Street segment combinations' correlation coefficients ranged 0.75-1.0. Significant positive correlations were found between overall neighborhood quality scores, four of the five Checklist subsection scores, and Walk Scores® (r = 0.62, p < 0.001). This scoring method adequately captures neighborhood features in low-income, residential areas and may aid in delineating impact of specific built environment features on health behaviors and outcomes.

SUBMITTER: Adu-Brimpong J

PROVIDER: S-EPMC5369109 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Optimizing Scoring and Sampling Methods for Assessing Built Neighborhood Environment Quality in Residential Areas.

Adu-Brimpong Joel J Coffey Nathan N Ayers Colby C Berrigan David D Yingling Leah R LR Thomas Samantha S Mitchell Valerie V Ahuja Chaarushi C Rivers Joshua J Hartz Jacob J Powell-Wiley Tiffany M TM

International journal of environmental research and public health 20170308 3

Optimization of existing measurement tools is necessary to explore links between aspects of the neighborhood built environment and health behaviors or outcomes. We evaluate a scoring method for virtual neighborhood audits utilizing the Active Neighborhood Checklist (the Checklist), a neighborhood audit measure, and assess street segment representativeness in low-income neighborhoods. Eighty-two home neighborhoods of Washington, D.C. Cardiovascular Health/Needs Assessment (NCT01927783) participan ...[more]

PMID: 28282878

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Project description:Objective To examine associations between neighborhood built environment (BE) variables, residential property values, and longitudinal 1- and 2-year changes in body mass index (BMI). Methods The Seattle Obesity Study III was a prospective cohort study of adults with geocoded residential addresses, conducted in King, Pierce, and Yakima Counties in Washington State. Measured heights and weights were obtained at baseline (n = 879), year 1 (n = 727), and year 2 (n = 679). Tax parcel residential property values served as proxies for individual socioeconomic status. Residential unit and road intersection density were captured using Euclidean-based SmartMaps at 800 m buffers. Counts of supermarket (0 versus. 1+) and fast-food restaurant availability (0, 1–3, 4+) were measured using network based SmartMaps at 1600 m buffers. Density measures and residential property values were categorized into tertiles. Linear mixed-effects models tested whether baseline BE variables and property values were associated with differential changes in BMI at year 1 or year 2, adjusting for age, gender, race/ethnicity, education, home ownership, and county of residence. These associations were then tested for potential disparities by age group, gender, race/ethnicity, and education. Results Road intersection density, access to food sources, and residential property values were inversely associated with BMI at baseline. At year 1, participants in the 3rd tertile of density metrics and with 4+ fast-food restaurants nearby showed less BMI gain compared to those in the 1st tertile or with 0 restaurants. At year 2, higher residential property values were predictive of lower BMI gain. There was evidence of differential associations by age group, gender, and education but not race/ethnicity. Conclusion Inverse associations between BE metrics and residential property values at baseline demonstrated mixed associations with 1- and 2-year BMI change. More work is needed to understand how individual-level sociodemographic factors moderate associations between the BE, property values, and BMI change. Highlights • Strong, inverse cross-sectional relationships between the built environment, residential property values (a proxy for individual socioeconomic status), and measured BMI were observed.• Measures of the built environment and residential property values showed modest and inconsistent associations with 1- and 2-year BMI change.• There was suggestive evidence that age may moderate the association between urban density and 1- and 2-year BMI change while education may moderate the association between residential property values and 2-year BMI change.

Dataset Information

Optimizing Scoring and Sampling Methods for Assessing Built Neighborhood Environment Quality in Residential Areas.

Publications

Optimizing Scoring and Sampling Methods for Assessing Built Neighborhood Environment Quality in Residential Areas.

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