Moudon, Anne Vernez; Cook, Andrea J.; Ulmer, Jared; Hurvitz, Philip M.; Drewnowski, Adam. (2011). A Neighborhood Wealth Metric for Use in Health Studies. American Journal Of Preventive Medicine, 41(1), 88 – 97.
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Abstract
Background: Measures of neighborhood deprivation used in health research are typically based on conventional area-based SES. Purpose: The aim of this study is to examine new data and measures of SES for use in health research. Specifically, assessed property values are introduced as a new individual-level metric of wealth and tested for their ability to substitute for conventional area-based SES as measures of neighborhood deprivation. Methods: The analysis was conducted in 2010 using data from 1922 participants in the 2008-2009 survey of the Seattle Obesity Study (SOS). It compared the relative strength of the association between the individual-level neighborhood wealth metric (assessed property values) and area-level SES measures (including education, income, and percentage above poverty as single variables, and as the composite Singh index) on the binary outcome fair/poor general health status. Analyses were adjusted for gender, categoric age, race, employment status, home ownership, and household income. Results: The neighborhood wealth measure was more predictive of fair/poor health status than area-level SES measures, calculated either as single variables or as indices (lower DIC measures for all models). The odds of having a fair/poor health status decreased by 0.85 (95% CI=0.77, 0.93) per $50,000 increase in neighborhood property values after adjusting for individual-level SES measures. Conclusions: The proposed individual-level metric of neighborhood wealth, if replicated in other areas, could replace area-based SES measures, thus simplifying analyses of contextual effects on health. (Am J Prev Med 2011; 41(1): 88-97) (C) 2011 American Journal of Preventive Medicine
Keywords
Health -- Social Aspects; Social Status; Public Health Research; Home Ownership; Income; Real Property; Deprivation (psychology); Health Education; Disparities Geocoding Project; Body-mass Index; Socioeconomic-status; Ecological Fallacy; Built Environment; Deprivation Indexes; Multilevel Analysis; Individual-level; Social-class; Inequalities
Duncan, Glen E.; Mills, Brianna; Strachan, Eric; Hurvitz, Philip; Huang, Ruizhu; Moudon, Anne Vernez; Turkheimer, Eric. (2014). Stepping Towards Causation in Studies of Neighborhood and Environmental Effects: How Twin Research Can Overcome Problems of Selection and Reverse Causation. Health & Place, 27, 106 – 111.
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Abstract
No causal evidence is available to translate associations between neighborhood characteristics and health outcomes into beneficial changes to built environments. Observed associations may be causal or result from uncontrolled confounds related to family upbringing. Twin designs can help neighborhood effects studies overcome selection and reverse causation problems in specifying causal mechanisms. Beyond quantifying genetic effects (i.e., heritability coefficients), we provide examples of innovative measures and analytic methods that use twins as quasi-experimental controls for confounding by environmental effects. We conclude that collaboration among investigators from multiple fields can move the field forward by designing studies that step toward causation. (C) 2014 Elsevier Ltd. All rights reserved,
Keywords
Residential Location; Methylation; Gene; Interplay; Obesity; Causality; Environment Design; Lifestyle Risk Reduction; Social And Built Environments; Twin Studies
Hwang, Liang-dar; Hurvitz, Philip M.; Duncan, Glen E. (2016). Cross Sectional Association between Spatially Measured Walking Bouts and Neighborhood Walkability. International Journal Of Environmental Research And Public Health, 13(4).
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Abstract
Walking is the most popular choice of aerobic physical activity to improve health among U.S. adults. Physical characteristics of the home neighborhood can facilitate or hinder walking. The purpose of this study was to quantify neighborhood walking, using objective methods and to examine the association between counts of walking bouts in the home neighborhood and neighborhood walkability. This was a cross-sectional study of 106 adults who wore accelerometers and GPS devices for two weeks. Walking was quantified within 1, 2, and 3 km Euclidean (straight-line) and network buffers around the geocoded home location. Walkability was estimated using a commercially available index. Walking bout counts increased with buffer size and were associated with walkability, regardless of buffer type or size (p < 0.001). Quantification of walking bouts within (and outside) of pre-defined neighborhood buffers of different sizes and types allowed for the specification of walking locations to better describe and elucidate walking behaviors. These data support the concept that neighborhood characteristics can influence walking among adults.
Keywords
Physical-activity; Accelerometer Data; United-states; Urban Form; Land-use; Validation; Health; Transportation; Environments; Intensity; Geographic Information Systems; Residence Characteristics; Twins; Walking
Kang, Bumjoon; Moudon, Anne V.; Hurvitz, Philip M.; Saelens, Brian E. (2018). Increased Walking’s Additive and No Substitution Effect on Total Physical Activity. Medicine & Science In Sports & Exercise, 50(3), 468 – 475.
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Abstract
Purpose We assessed the associations between a change in time spent walking and a change in total physical activity (PA) time within an urban living adult sample to test for additive or substitution effects. Methods Participants living in the greater Seattle area were assessed in 2008-2009 and again 1-2 yr later (2010-2011). At each time point, they wore accelerometers and GPS units and recorded trips and locations in a travel diary for seven consecutive days. These data streams were combined to derive a more objective estimate of walking and total PA. Participants also completed the International Physical Activity Questionnaire to provide self-reported estimates of walking and total PA. Regression analyses assessed the associations between within-participant changes in objective and self-reported walking and total PA. Results Data came from 437 participants. On average, a 1-min increase in total walking was associated with an increase in total PA of 1 min, measured by objective data, and 1.2-min, measured by self-reported data. A similar additive effect was consistently found with utilitarian, transportation, or job-related walking, measured by both objective and self-reported data. For recreational walking, the effect of change was mixed between objective and self-reported results. Conclusion Both objective and self-reported data confirmed an additive effect of utilitarian and total walking on PA.
Keywords
Accelerometers; Global Positioning System; Metropolitan Areas; Questionnaires; Recreation; Self-evaluation; Time; Walking; Physical Activity; Accelerometer; Gps; Ipaq; Longitudinal Study; Self-reported Measures; Light-rail Construction; Built Environment; Accelerometer Data; Older-adults; Urban Form; Transit Use; Travel; Neighborhood; Interventions; Calibration
Scully, Jason Y.; Moudon, Anne Vernez; Hurvitz, Philip M.; Aggarwal, Anju; Drewnowski, Adam. (2019). A Time-Based Objective Measure of Exposure to the Food Environment. International Journal Of Environmental Research And Public Health, 16(7).
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Abstract
Exposure to food environments has mainly been limited to counting food outlets near participants' homes. This study considers food environment exposures in time and space using global positioning systems (GPS) records and fast food restaurants (FFRs) as the environment of interest. Data came from 412 participants (median participant age of 45) in the Seattle Obesity Study II who completed a survey, wore GPS receivers, and filled out travel logs for seven days. FFR locations were obtained from Public Health Seattle King County and geocoded. Exposure was conceptualized as contact between stressors (FFRs) and receptors (participants' mobility records from GPS data) using four proximities: 21 m, 100 m, 500 m, and 1/2 mile. Measures included count of proximal FFRs, time duration in proximity to 1 FFR, and time duration in proximity to FFRs weighted by FFR counts. Self-reported exposures (FFR visits) were excluded from these measures. Logistic regressions tested associations between one or more reported FFR visits and the three exposure measures at the four proximities. Time spent in proximity to an FFR was associated with significantly higher odds of FFR visits at all proximities. Weighted duration also showed positive associations with FFR visits at 21-m and 100-m proximities. FFR counts were not associated with FFR visits. Duration of exposure helps measure the relationship between the food environment, mobility patterns, and health behaviors. The stronger associations between exposure and outcome found at closer proximities (<100 m) need further research.
Keywords
Global Positioning Systems; Physical-activity; Health Research; Land-use; Neighborhood; Gps; Obesity; Tracking; Validity; Mobility; Fast Food; Spatio-temporal Exposure; Mobility Patterns; Selective Mobility Bias
Wang, Lan; Zhang, Surong; Yang, Zilin; Zhao, Ziyu; Moudon, Anne Vernez; Feng, Huasen; Liang, Junhao; Sun, Wenyao; Cao, Buyang. (2021). What County-level Factors Influence Covid-19 Incidence in the United States? Findings from the First Wave of the Pandemic. Cities, 118.
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Abstract
Effective control of the COVID-19 pandemic via appropriate management of the built environment is an urgent issue. This study develops a research framework to explore the relationship between COVID-19 incidence and influential factors related to protection of vulnerable populations, intervention in transmission pathways, and provision of healthcare resources. Relevant data for regression analysis and structural equation modeling is collected during the first wave of the pandemic in the United States, from counties with over 100 confirmed cases. In addition to confirming certain factors found in the existing literature, we uncover six new factors significantly associated with COVID-19 incidence. Furthermore, incidence during the lockdown is found to significantly affect incidence after the reopening, highlighting that timely quarantining and treating of patients is essential to avoid the snowballing transmission over time. These findings suggest ways to mitigate the negative effects of subsequent waves of the pandemic, such as special attention of infection prevention in neighborhoods with unsanitary and overcrowded housing, minimization of social activities organized by neighborhood associations, and contactless home delivery service of healthy food. Also worth noting is the need to provide support to people less capable of complying with the stay-at-home order because of their occupations or socio-economic disadvantage.
Keywords
Pandemics; Covid-19; Covid-19 Pandemic; Infection Prevention; Stay-at-home Orders; Structural Equation Modeling; United States; Communicable Disease Prevention; Influential Factors; Lockdown; Structural Equation Modeling (sem); Prevalence; Disease; Healthy Food; Social Activities; Counties; Neighborhoods; Housing; Built Environment; Prevention; Minimization; Socioeconomic Factors; Intervention; Health Care; Vulnerability; Occupations; Coronaviruses; Food Service; Disease Transmission; United States--us
Drewnowski, Adam; Aggarwal, Anju; Hurvitz, Philip M.; Monsivais, Pablo; Moudon, Anne V. (2012). Obesity and Supermarket Access: Proximity or Price? American Journal Of Public Health, 102(8), e74 – e80.
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Abstract
Objectives. We examined whether physical proximity to supermarkets or supermarket price was more strongly associated with obesity risk. Methods. The Seattle Obesity Study (SOS) collected and geocoded data on home addresses and food shopping destinations for a representative sample of adult residents of King County, Washington. Supermarkets were stratified into 3 price levels based on average cost of the market basket. Sociodemographic and health data were obtained from a telephone survey. Modified Poisson regression was used to test the associations between obesity and supermarket variables. Results. Only 1 in 7 respondents reported shopping at the nearest supermarket. The risk of obesity was not associated with street network distances between home and the nearest supermarket or the supermarket that SOS participants reported as their primary food source. The type of supermarket, by price, was found to be inversely and significantly associated with obesity rates, even after adjusting for individual-level sociodemographic and lifestyle variables, and proximity measures (adjusted relative risk = 0.34; 95% confidence interval = 0.19, 0.63) Conclusions. Improving physical access to supermarkets may be one strategy to deal with the obesity epidemic; improving economic access to healthy foods is another. [ABSTRACT FROM AUTHOR]; Copyright of American Journal of Public Health is the property of American Public Health Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Keywords
Natural Foods; Obesity Risk Factors; Surveys; Cluster Analysis (statistics); Confidence Intervals; Correlation (statistics); Food Service; Geographic Information Systems; Poisson Distribution; Population Geography; Research Funding; User Charges; Residential Patterns; Socioeconomic Factors; Relative Medical Risk; Statistical Models; Descriptive Statistics; Economics; Washington (state)
Hurvitz, Philip M.; Moudon, Anne V.; Kang, Bumjoon; Fesinmeyer, Megan D.; Saelens, Brian E. (2014). How Far from Home? The Locations of Physical Activity in an Urban US Setting. Preventive Medicine, 69, 181 – 186.
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Abstract
Little is known about where physical activity (PA) occurs, or whether different demographic groups accumulate PA in different locations. Method. Objective data on PA and location from 611 adults over 7 days were collected in King County, WA in 2008-2009. The relative amounts of time spent in sedentary-to-low and moderate-to-vigorous PA (MVPA) were quantified at three locations: home (1666 m). Differences in MVPA by demographics and location were examined. The percent of daily time in MVPA was estimated using a mixed model adjusted for location, sex, age, race/ethnicity, employment, education, BMI, and income. Results. Most MVPA time occurred in nonhome locations, and disproportionately near home; this location was associated with 16.46% greater time in MVPA, compared to at-home activity (p< 0.001), whereas more time spent at away locations was associated with 3.74% greater time in MVPA (p< 0.001). Location was found to be a predictor of MVPA independent of demographic factors. Conclusion. A large proportion of MVPA time is spent at near locations, corresponding to the home neighborhood studied in previous PA research. Away locations also host time spent in MVPA and should be the focus of future research. (C) 2014 Elsevier Inc All rights reserved.
Keywords
Accelerometer Data; Built Environment; United-states; Neighborhood Walkability; Exercise Intensity; Time Use; Land-use; Walking; Health; Behavior; Physical Activity; Objective Measurement; Gps; Accelerometry; Gis
James, Peter; Jankowska, Marta; Marx, Christine; Hart, Jaime E.; Berrigan, David; Kerr, Jacqueline; Hurvitz, Philip M.; Hipp, J. Aaron; Laden, Francine. (2016). Spatial Energetics Integrating Data from GPS, Accelerometry, and GIS to Address Obesity and Inactivity. American Journal Of Preventive Medicine, 51(5), 792 – 800.
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Abstract
To address the current obesity and inactivity epidemics, public health researchers have attempted to identify spatial factors that influence physical inactivity and obesity. Technologic and methodologic developments have led to a revolutionary ability to examine dynamic, high-resolution measures of temporally matched location and behavior data through GPS, accelerometry, and GIS. These advances allow the investigation of spatial energetics, high-spatiotemporal resolution data on location and time-matched energetics, to examine how environmental characteristics, space, and time are linked to activity-related health behaviors with far more robust and detailed data than in previous work. Although the transdisciplinary field of spatial energetics demonstrates promise to provide novel insights on how individuals and populations interact with their environment, there remain significant conceptual, technical, analytical, and ethical challenges stemming from the complex data streams that spatial energetics research generates. First, it is essential to better understand what spatial energetics data represent, the relevant spatial context of analysis for these data, and if spatial energetics can establish causality for development of spatially relevant interventions. Second, there are significant technical problems for analysis of voluminous and complex data that may require development of spatially aware scalable computational infrastructures. Third, the field must come to agreement on appropriate statistical methodologies to account for multiple observations per person. Finally, these challenges must be considered within the context of maintaining participant privacy and security. This article describes gaps in current practice and understanding and suggests solutions to move this promising area of research forward. (C) 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
Keywords
Physical-activity Levels; Built Environment; Activity Monitors; Travel Behavior; Health Research; Neighborhood; Exposure; Validation; Children; Design
Kang, Mingyu; Moudon, Anne V.; Hurvitz, Philip M.; Saelens, Brian E. (2018). Capturing Fine-Scale Travel Behaviors: A Comparative Analysis between Personal Activity Location Measurement System (PALMS) and Travel Diary. International Journal Of Health Geographics, 17(1).
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Abstract
BackgroundDevice-collected data from GPS and accelerometers for identifying active travel behaviors have dramatically changed research methods in transportation planning and public health. Automated algorithms have helped researchers to process large datasets with likely fewer errors than found in other collection methods (e.g., self-report travel diary). In this study, we compared travel modes identified by a commonly used automated algorithm (PALMS) that integrates GPS and accelerometer data with those obtained from travel diary estimates.MethodsSixty participants, who made 2100 trips during seven consecutive days of data collection, were selected from among the baseline sample of a project examining the travel behavior impact of a new light rail system in the greater Seattle, WA (USA) area. GPS point level analyses were first conducted to compare trip/place and travel mode detection results using contingency tables. Trip level analyses were then performed to investigate the effect of proportions of time overlap between travel logs and device-collected data on agreement rates. Global performance (with all subjects' data combined) and subject-level performance of the algorithm were compared at the trip level.ResultsAt the GPS point level, the overall agreement rate of travel mode detection was 77.4% between PALMS and the travel diary. The agreement rate for vehicular trip detection (84.5%) was higher than for bicycling (53.5%) and walking (58.2%). At the trip level, the global performance and subject-level performance of the PALMS algorithm were 46.4% and 42.4%, respectively. Vehicular trip detection showed highest agreement rates in all analyses. Study participants' primary travel mode and car ownership were significantly related to the subject-level mode agreement rates.ConclusionsThe PALMS algorithm showed moderate identification power at the GPS point level. However, trip level analyses found lower agreement rates between PALMS and travel diary data, especially for active transportation. Testing different PALMS parameter settings may serve to improve the detection of active travel and help expand PALMS's applicability in geographically different urbanized areas with a variety of travel modes.
Keywords
Transportation Planning; Public Health; Accelerometers; Global Positioning System; Voyages & Travels; Cycling; Algorithms; Accelerometer; Automated Algorithm; Gis; Gps; Places; Trips; Global Positioning Systems; Physical-activity; Data-collection; Health Research; Gps Data; Accelerometry; Validity