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dc.contributor.authorLuan, Hui
dc.contributor.authorLaw, Jane
dc.contributor.authorLysy, Martin
dc.date.accessioned2018-10-22 12:09:50 (GMT)
dc.date.available2018-10-22 12:09:50 (GMT)
dc.date.issued2018-02-01
dc.identifier.urihttps://dx.doi.org/10.1016/j.sste.2017.12.001
dc.identifier.urihttp://hdl.handle.net/10012/14010
dc.descriptionThe final publication is available at Elsevier via https://dx.doi.org/10.1016/j.sste.2017.12.001 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.description.abstractNeighborhood restaurant environment (NRE) plays a vital role in shaping residents' eating behaviors. While NRE 'healthfulness' is a multi-facet concept, most studies evaluate it based only on restaurant type, thus largely ignoring variations of in-restaurant features. In the few studies that do account for such features, healthfulness scores are simply averaged over accessible restaurants, thereby concealing any uncertainty that attributed to neighborhoods' size or spatial correlation. To address these limitations, this paper presents a Bayesian Spatial Factor Analysis for assessing NRE healthfulness in the city of Kitchener, Canada. Several in-restaurant characteristics are included. By treating NRE healthfulness as a spatially correlated latent variable, the adopted modeling approach can: (i) identify specific indicators most relevant to NRE healthfulness, (ii) provide healthfulness estimates for neighborhoods without accessible restaurants, and (iii) readily quantify uncertainties in the healthfulness index. Implications of the analysis for intervention program development and community food planning are discussed. (c) 2017 Elsevier Ltd. All rights reserved.en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectNeighborhood restaurant environmenten
dc.subjectConsumer nutrition environmenten
dc.subjectBayesian inferenceen
dc.subjectSpatial modelingen
dc.subjectFactor analysisen
dc.subjectCommunity food planningen
dc.titleDiving into the consumer nutrition environment: A Bayesian spatial factor analysis of neighborhood restaurant environmenten
dc.typeArticleen
dcterms.bibliographicCitationLuan, H., Law, J., & Lysy, M. (2018). Diving into the consumer nutrition environment: A Bayesian spatial factor analysis of neighborhood restaurant environment. Spatial and Spatio-Temporal Epidemiology, 24, 39–51. doi:10.1016/j.sste.2017.12.001en
uws.contributor.affiliation1Faculty of Applied Health Sciencesen
uws.contributor.affiliation1Faculty of Mathematicsen
uws.contributor.affiliation1Faculty of Environmenten
uws.contributor.affiliation2School of Public Health and Health Systemsen
uws.contributor.affiliation2Statistics and Actuarial Scienceen
uws.contributor.affiliation2School of Planningen
uws.typeOfResourceTexten
uws.typeOfResourceTexten
uws.peerReviewStatusRevieweden
uws.scholarLevelFacultyen


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