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

Joel Adu-Brimpong,Joshua Rivers,Chaarushi Ahuja,Leah Yingling,Jacob Hartz,Colby Ayers,Valerie Mitchell,Nathan Coffey,David Berrigan,Samantha Thomas,Tiffany Powell-Wiley

doi:10.3390/ijerph14030273

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.

Highlights

Socio-ecological models posit that the social and physical environments that we inhabit influence our health in many important ways [1,2,3,4]
We examined correlation(s) between study participants’ virtual audit scores and Walk Scores® using Spearman calculations as an additional measure to further evaluate the reliability of our scoring method
Our results suggest that the proposed scoring method for the Checklist is a reliableis means of means of obtaining neighborhood built environment quality information specific neighborhood obtaining neighborhood built environment quality information for specificfor neighborhood addresses

Summary

Introduction

Socio-ecological models posit that the social and physical environments that we inhabit influence our health in many important ways [1,2,3,4]. Studies have observed associations between residing in highly walkable neighborhoods and health outcomes such as lower abdominal obesity [8], prevalence of overweight and obesity and incidence of diabetes [9]. The mechanism(s) linking specific aspects of the built neighborhood environment and health-related behaviors and outcomes are not well understood. To gain insights into the neighborhood correlates of health behaviors and outcomes, recent observational studies have used omnidirectional imagery from virtual platforms such as Google With physical inactivity looming as a 21st century public health priority [10], it is imperative that we understand environmental factors that enhance and or impede PA engagement among diverse populations.

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Conclusion