Dustin T Duncan1, Kosuke Tamura2, Seann D Regan2, Jessica Athens2, Brian Elbel3, Julie Meline4, Yazan A Al-Ajlouni2, Basile Chaix4. 1. Department of Population Health, New York University School of Medicine, New York. Electronic address: Dustin.Duncan@nyumc.org. 2. Department of Population Health, New York University School of Medicine, New York. 3. Department of Population Health, New York University School of Medicine, New York; Wagner Graduate School of Public Service, New York University, New York. 4. Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Pierre Louis Institute of Epidemiology and Public Health, Paris, France; Inserm, UMR_S 1136, Pierre Louis Institute of Epidemiology and Public Health, Paris, France.
Abstract
PURPOSE: To examine if there was spatial misclassification in exposure to neighborhood noise complaints among a sample of low-income housing residents in New York City, comparing home-based spatial buffers and Global Positioning System (GPS) daily path buffers. METHODS: Data came from the community-based NYC Low-Income Housing, Neighborhoods and Health Study, where GPS tracking of the sample was conducted for a week (analytic n = 102). We created a GPS daily path buffer (a buffering zone drawn around GPS tracks) of 200 m and 400 m. We also used home-based buffers of 200 m and 400 m. Using these "neighborhoods" (or exposure areas), we calculated neighborhood exposure to noisy events from 311 complaints data (analytic n = 143,967). Friedman tests (to compare overall differences in neighborhood definitions) were applied. RESULTS: There were differences in neighborhood noise complaints according to the selected neighborhood definitions (P < .05). For example, the mean neighborhood noise complaint count was 1196 per square kilometer for the 400-m home-based and 812 per square kilometer for the 400-m activity space buffer, illustrating how neighborhood definition influences the estimates of exposure to neighborhood noise complaints. CONCLUSIONS: These analyses suggest that, whenever appropriate, GPS neighborhood definitions can be used in spatial epidemiology research in spatially mobile populations to understand people's lived experience.
PURPOSE: To examine if there was spatial misclassification in exposure to neighborhood noise complaints among a sample of low-income housing residents in New York City, comparing home-based spatial buffers and Global Positioning System (GPS) daily path buffers. METHODS: Data came from the community-based NYC Low-Income Housing, Neighborhoods and Health Study, where GPS tracking of the sample was conducted for a week (analytic n = 102). We created a GPS daily path buffer (a buffering zone drawn around GPS tracks) of 200 m and 400 m. We also used home-based buffers of 200 m and 400 m. Using these "neighborhoods" (or exposure areas), we calculated neighborhood exposure to noisy events from 311 complaints data (analytic n = 143,967). Friedman tests (to compare overall differences in neighborhood definitions) were applied. RESULTS: There were differences in neighborhood noise complaints according to the selected neighborhood definitions (P < .05). For example, the mean neighborhood noise complaint count was 1196 per square kilometer for the 400-m home-based and 812 per square kilometer for the 400-m activity space buffer, illustrating how neighborhood definition influences the estimates of exposure to neighborhood noise complaints. CONCLUSIONS: These analyses suggest that, whenever appropriate, GPS neighborhood definitions can be used in spatial epidemiology research in spatially mobile populations to understand people's lived experience.
Authors: Dustin T Duncan; Ichiro Kawachi; S V Subramanian; Jared Aldstadt; Steven J Melly; David R Williams Journal: Am J Epidemiol Date: 2013-10-22 Impact factor: 4.897
Authors: Dustin T Duncan; Marcia C Castro; Jeffrey C Blossom; Gary G Bennett; Steven L Gortmaker Journal: Geospat Health Date: 2011-05 Impact factor: 1.212
Authors: Dustin T Duncan; Ryan R Ruff; Basile Chaix; Seann D Regan; James H Williams; Joseph Ravenell; Marie A Bragg; Gbenga Ogedegbe; Brian Elbel Journal: Geospat Health Date: 2016-05-31 Impact factor: 1.212
Authors: Dustin T Duncan; Farzana Kapadia; Seann D Regan; William C Goedel; Michael D Levy; Staci C Barton; Samuel R Friedman; Perry N Halkitis Journal: PLoS One Date: 2016-02-26 Impact factor: 3.240
Authors: Flo Harrison; Thomas Burgoine; Kirsten Corder; Esther M F van Sluijs; Andy Jones Journal: Int J Health Geogr Date: 2014-02-14 Impact factor: 3.918
Authors: Dustin T Duncan; Basile Chaix; Seann D Regan; Su Hyun Park; Cordarian Draper; William C Goedel; June A Gipson; Vincent Guilamo-Ramos; Perry N Halkitis; Russell Brewer; DeMarc A Hickson Journal: AIDS Behav Date: 2018-09
Authors: Kosuke Tamura; Brian Elbel; Basile Chaix; Seann D Regan; Yazan A Al-Ajlouni; Jessica K Athens; Julie Meline; Dustin T Duncan Journal: J Community Health Date: 2017-10
Authors: William C Goedel; Sari L Reisner; Aron C Janssen; Tonia C Poteat; Seann D Regan; Noah T Kreski; Gladyne Confident; Dustin T Duncan Journal: Transgend Health Date: 2017-07-01
Authors: Dustin T Duncan; DeMarc A Hickson; William C Goedel; Denton Callander; Brandon Brooks; Yen-Tyng Chen; Hillary Hanson; Rebecca Eavou; Aditya S Khanna; Basile Chaix; Seann D Regan; Darrell P Wheeler; Kenneth H Mayer; Steven A Safren; Sandra Carr Melvin; Cordarian Draper; Veronica Magee-Jackson; Russell Brewer; John A Schneider Journal: Int J Environ Res Public Health Date: 2019-05-30 Impact factor: 3.390
Authors: Tiffany M Powell-Wiley; Yvonne Baumer; Foster Osei Baah; Andrew S Baez; Nicole Farmer; Christa T Mahlobo; Mario A Pita; Kameswari A Potharaju; Kosuke Tamura; Gwenyth R Wallen Journal: Circ Res Date: 2022-03-03 Impact factor: 17.367