Optimizing Methods for Neighborhood Built Environment Virtual Audits: Data from the Washington D.C. Cardiovascular Health and Needs Assessment

Authors

• J Adu-Brimpong
• N Coffey
• D Berrigan
• C Ayers
• L Yingling
• S Thomas
• V Mitchell
• T Powell-Wiley

Abstract

Background:Virtual audits (i.e.,combining Google Maps imagery with validated neighborhood audit measures) can efficiently characterize neighborhood built environment quality. However, little is known about scoring or sampling methods to conduct virtual audits of specific neighborhood addresses. Methods: We developed a scoring and sampling method for the Active Neighborhood Checklist (the Checklist) using audits of participants’ neighborhoods in the D.C. Cardiovascular Health and Needs Assessment (NCT01927783). Twelve street segments adjacent to 36 participants’ home addresses were audited using Google Street View imagery and the Checklist, a validated audit tool with 45 questions assessing built environment features (i.e.,land-use type and street characteristics). 432 street segments are included in this study. Scores ranged 0-2 points per question. Scores of the 12 street segments were aggregated to calculate an overall built environment quality score per address. Combinatorial coding was developed to assess the relationship(s) between street segment scores and overall built environment score per address. Correlation coefficients were obtained using Spearman calculations. Results: Inter-rater reliability, PABAK (kappa), for the virtual audits was 0.88. Maximum possible score per street segment is 87 points; participants’ scores ranged 16-45(Mean=32.01± 4.88). Maximum audit score for 12 segments is 1044 points; participants’ scores ranged 333-472(Mean=384.2±34.3). Correlation coefficients obtained from segment combinations ranged 0.58-1.0, with r=.78 for any three randomly-generated street segments per address. Conclusions: This study demonstrates a potential scoring method for virtual audits utilizing the Checklist and highlights that three street segments in low-income, residential areas in Washington, D.C. may provide sufficient information to assess neighborhood built environment quality.

Scientific Focus Area: Epidemiology

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