A Reliable, Feasible Method to Observe Neighborhoods at High Spatial Resolution.

Autor: Kepper MM; Behavioral and Community Health Sciences Department, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana. Electronic address: mmohle@lsuhsc.edu., Sothern MS; Behavioral and Community Health Sciences Department, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana., Theall KP; Global Community Health and Behavioral Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana., Griffiths LA; Behavioral and Community Health Sciences Department, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana., Scribner RA; Department of Epidemiology, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana., Tseng TS; Behavioral and Community Health Sciences Department, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana., Schaettle P; Global Community Health and Behavioral Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana., Cwik JM; Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana., Felker-Kantor E; Global Community Health and Behavioral Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana., Broyles ST; Contextual Risk Factors Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana.
Jazyk: angličtina
Zdroj: American journal of preventive medicine [Am J Prev Med] 2017 Jan; Vol. 52 (1S1), pp. S20-S30.
DOI: 10.1016/j.amepre.2016.06.010
Abstrakt: Introduction: Systematic social observation (SSO) methods traditionally measure neighborhoods at street level and have been performed reliably using virtual applications to increase feasibility. Research indicates that collection at even higher spatial resolution may better elucidate the health impact of neighborhood factors, but whether virtual applications can reliably capture social determinants of health at the smallest geographic resolution (parcel level) remains uncertain. This paper presents a novel, parcel-level SSO methodology and assesses whether this new method can be collected reliably using Google Street View and is feasible.
Methods: Multiple raters (N=5) observed 42 neighborhoods. In 2016, inter-rater reliability (observed agreement and kappa coefficient) was compared for four SSO methods: (1) street-level in person; (2) street-level virtual; (3) parcel-level in person; and (4) parcel-level virtual. Intra-rater reliability (observed agreement and kappa coefficient) was calculated to determine whether parcel-level methods produce results comparable to traditional street-level observation.
Results: Substantial levels of inter-rater agreement were documented across all four methods; all methods had >70% of items with at least substantial agreement. Only physical decay showed higher levels of agreement (83% of items with >75% agreement) for direct versus virtual rating source. Intra-rater agreement comparing street- versus parcel-level methods resulted in observed agreement >75% for all but one item (90%).
Conclusions: Results support the use of Google Street View as a reliable, feasible tool for performing SSO at the smallest geographic resolution. Validation of a new parcel-level method collected virtually may improve the assessment of social determinants contributing to disparities in health behaviors and outcomes.
(Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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