Anne Vernez Moudon Archives - Page 2 of 9

City Planning Policies to Support Health and Sustainability: An International Comparison of Policy Indicators for 25 Cities

Lowe, Melanie; Adlakha, Deepti; Sallis, James F.; Salvo, Deborah; Cerin, Ester; Moudon, Anne Vernez; Higgs, Carl; Hinckson, Erica; Arundel, Jonathan; Boeing, Geoff; Liu, Shiqin; Mansour, Perla; Gebel, Klaus; Puig-ribera, Anna; Mishra, Pinki Bhasin; Bozovic, Tamara; Carson, Jacob; Dygryn, Jan; Florindo, Alex A.; Ho, Thanh Phuong; Hook, Hannah; Hunter, Ruth F.; Lai, Poh-chin; Molina-garcia, Javier; Nitvimol, Kornsupha; Oyeyemi, Adewale L.; Ramos, Carolina D. G.; Resendiz, Eugen; Troelsen, Jens; Witlox, Frank; Giles-corti, Billie. (2022). City Planning Policies to Support Health and Sustainability: An International Comparison of Policy Indicators for 25 Cities. Lancet Global Health, 10(6), E882-E894.

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Abstract

City planning policies influence urban lifestyles, health, and sustainability. We assessed policy frameworks for city planning for 25 cities across 19 lower-middle-income countries, upper-middle-income countries, and high-income countries to identify whether these policies supported the creation of healthy and sustainable cities. We systematically collected policy data for evidence-informed indicators related to integrated city planning, air pollution, destination accessibility, distribution of employment, demand management, design, density, distance to public transport, and transport infrastructure investment. Content analysis identified strengths, limitations, and gaps in policies, allowing us to draw comparisons between cities. We found that despite common policy rhetoric endorsing healthy and sustainable cities, there was a paucity of measurable policy targets in place to achieve these aspirations. Some policies were inconsistent with public health evidence, which sets up barriers to achieving healthy and sustainable urban environments. There is an urgent need to build capacity for health-enhancing city planning policy and governance, particularly in low-income and middle-income countries.

Keywords

Physical-activity; Population Health; Walkability

Deciphering the Impact of Urban Built Environment Density on Respiratory Health Using a Quasi-cohort Analysis of 5495 Non-smoking Lung Cancer Cases

Wang, Lan; Sun, Wenyao; Moudon, Anne Vernez; Zhu, Yong-guan; Wang, Jinfeng; Bao, Pingping; Zhao, Xiaojing; Yang, Xiaoming; Jia, Yinghui; Zhang, Surong; Wu, Shuang; Cai, Yuxi. (2022). Deciphering the Impact of Urban Built Environment Density on Respiratory Health Using a Quasi-cohort Analysis of 5495 Non-smoking Lung Cancer Cases. Science Of The Total Environment, 850.

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Abstract

Introduction: Lung cancer is a major health concern and is influenced by air pollution, which can be affected by the den-sity of urban built environment. The spatiotemporal impact of urban density on lung cancer incidence remains unclear, especially at the sub-city level. We aimed to determine cumulative effect of community-level density attributes of the built environment on lung cancer incidence in high-density urban areas. Methods: We selected 78 communities in the central city of Shanghai, China as the study site; communities included in the analysis had an averaged population density of 313 residents per hectare. Using data from the city cancer surveil-lance system, an age-period-cohort analysis of lung cancer incidence was performed over a five-year period (2009-2013), with a total of 5495 non-smoking/non-secondhand smoking exposure lung cancer cases. Community -level density measures included the density of road network, facilities, buildings, green spaces, and land use mixture. Results: In multivariate models, built environment density and the exposure time duration had an interactive effect on lung cancer incidence. Lung cancer incidence of birth cohorts was associated with road density and building coverage across communities, with a relative risk of 1middot142 (95 % CI: 1middot056-1middot234, P = 0middot001) and 1middot090 (95 % CI: 1middot053-1middot128, P < 0middot001) at the baseline year (2009), respectively. The relative risk increased exponentially with the exposure timeduration. As for the change in lung cancer incidence over the five-year period, lung cancer incidence of birth cohorts tended to increase faster in communities with a higher road density and building coverage. Conclusion: Urban planning policies that improve road network design and building layout could be important strate-gies to reduce lung cancer incidence in high-density urban areas.

Keywords

Air-quality; Pollutant Dispersion; Risk-factors; Land-use; Mortality; Exposure; Cities; Transport; Compact City; Longitudinal Analysis; Lung Cancer; Urban Planning

Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study

Cruz, Maricela; Drewnowski, Adam; Bobb, Jennifer F.; Hurvitz, Philip M.; Moudon, Anne Vernez; Cook, Andrea; Mooney, Stephen J.; Buszkiewicz, James H.; Lozano, Paula; Rosenberg, Dori E.; Kapos, Flavia; Theis, Mary Kay; Anau, Jane; Arterburn, David. (2022). Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study. Epidemiology, 33(5), 747-755.

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Abstract

Background: Neighborhoods may play an important role in shaping long-term weight trajectory and obesity risk. Studying the impact of moving to another neighborhood may be the most efficient way to determine the impact of the built environment on health. We explored whether residential moves were associated with changes in body weight. Methods: Kaiser Permanente Washington electronic health records were used to identify 21,502 members aged 18-64 who moved within King County, WA between 2005 and 2017. We linked body weight measures to environment measures, including population, residential, and street intersection densities (800 m and 1,600 m Euclidian buffers) and access to supermarkets and fast foods (1,600 m and 5,000 m network distances). We used linear mixed models to estimate associations between postmove changes in environment and changes in body weight. Results: In general, moving from high-density to moderate- or low-density neighborhoods was associated with greater weight gain postmove. For example, those moving from high to low residential density neighborhoods (within 1,600 m) gained an average of 4.5 (95% confidence interval [CI] = 3.0, 5.9) lbs 3 years after moving, whereas those moving from low to high-density neighborhoods gained an average of 1.3 (95% CI = -0.2, 2.9) lbs. Also, those moving from neighborhoods without fast-food access (within 1600m) to other neighborhoods without fast-food access gained less weight (average 1.6 lbs [95% CI = 0.9, 2.4]) than those moving from and to neighborhoods with fast-food access (average 2.8 lbs [95% CI = 2.5, 3.2]). Conclusions: Moving to higher-density neighborhoods may be associated with reductions in adult weight gain.

Keywords

Body-mass Index; Neighborhood Socioeconomic-status; New-york-city; Built Environment; Physical-activity; Food Environment; Urban Sprawl; Risk-factors; Obesity; Walking; Electronic Medical Records; Fast Foods; Population Density; Residential Density; Residential Moves; Supermarkets

Using Open Data and Open-source Software to Develop Spatial Indicators of Urban Design and Transport Features for Achieving Healthy and Sustainable Cities

Boeing, Geoff; Higgs, Carl; Liu, Shiqin; Giles-corti, Billie; Sallis, James F.; Cerin, Ester; Lowe, Melanie; Adlakha, Deepti; Hinckson, Erica; Moudon, Anne Vernez; Salvo, Deborah; Adams, Marc A.; Barrozo, Ligia, V; Bozovic, Tamara; Delclos-alio, Xavier; Dygryn, Jan; Ferguson, Sara; Gebel, Klaus; Thanh Phuong Ho; Lai, Poh-chin; Martori, Joan C.; Nitvimol, Kornsupha; Queralt, Ana; Roberts, Jennifer D.; Sambo, Garba H.; Schipperijn, Jasper; Vale, David; Van De Weghe, Nico; Vich, Guillem; Arundel, Jonathan. (2022). Using Open Data and Open-source Software to Develop Spatial Indicators of Urban Design and Transport Features for Achieving Healthy and Sustainable Cities. Lancet Global Health, 10(6), E907-E918.

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Abstract

Benchmarking and monitoring of urban design and transport features is crucial to achieving local and international health and sustainability goals. However, most urban indicator frameworks use coarse spatial scales that either only allow between-city comparisons, or require expensive, technical, local spatial analyses for within-city comparisons. This study developed a reusable, open-source urban indicator computational framework using open data to enable consistent local and global comparative analyses. We show this framework by calculating spatial indicators-for 25 diverse cities in 19 countries-of urban design and transport features that support health and sustainability. We link these indicators to cities' policy contexts, and identify populations living above and below critical thresholds for physical activity through walking. Efforts to broaden participation in crowdsourcing data and to calculate globally consistent indicators are essential for planning evidence-informed urban interventions, monitoring policy effects, and learning lessons from peer cities to achieve health, equity, and sustainability goals.

Keywords

Systems; Access; Care

What Next? Expanding Our View of City Planning and Global Health, and Implementing and Monitoring Evidence-informed Policy

Giles-corti, Billie; Moudon, Anne Vernez; Lowe, Melanie; Cerin, Ester; Boeing, Geoff; Frumkin, Howard; Salvo, Deborah; Foster, Sarah; Kleeman, Alexandra; Bekessy, Sarah; De Sa, Thiago Herick; Nieuwenhuijsen, Mark; Higgs, Carl; Hinckson, Erica; Adlakha, Deepti; Arundel, Jonathan; Liu, Shiqin; Oyeyemi, Adewale L.; Nitvimol, Kornsupha; Sallis, James F. (2022). What Next? Expanding Our View of City Planning and Global Health, and Implementing and Monitoring Evidence-informed Policy. Lancet Global Health, 10(6), E919-E926.

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Abstract

This Series on urban design, transport, and health aimed to facilitate development of a global system of health-related policy and spatial indicators to assess achievements and deficiencies in urban and transport policies and features. This final paper in the Series summarises key findings, considers what to do next, and outlines urgent key actions. Our study of 25 cities in 19 countries found that, despite many well intentioned policies, few cities had measurable standards and policy targets to achieve healthy and sustainable cities. Available standards and targets were often insufficient to promote health and wellbeing, and health-supportive urban design and transport features were often inadequate or inequitably distributed. City planning decisions affect human and planetary health and amplify city vulnerabilities, as the COVID-19 pandemic has highlighted. Hence, we offer an expanded framework of pathways through which city planning affects health, incorporating 11 integrated urban system policies and 11 integrated urban and transport interventions addressing current and emerging issues. Our call to action recommends widespread uptake and further development of our methods and open-source tools to create upstream policy and spatial indicators to benchmark and track progress; unmask spatial inequities; inform interventions and investments; and accelerate transitions to net zero, healthy, and sustainable cities.

Home Versus Nonhome Neighborhood: Quantifying Differences in Exposure to the Built Environment

Hurvitz, Philip M.; Moudon, Anne Vernez. (2012). Home Versus Nonhome Neighborhood: Quantifying Differences in Exposure to the Built Environment. American Journal Of Preventive Medicine, 42(4), 411 – 417.

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Abstract

Background: Built environment and health research have focused on characteristics of home neighborhoods, whereas overall environmental exposures occur over larger spatial ranges. Purpose: Differences in built environment characteristics were analyzed for home and nonhome locations using GPS data. Methods: GPS data collected in 2007-2008 were analyzed for 41 subjects in the Seattle area in 2010. Environmental characteristics for 3.8 million locations were measured using novel GIS data sets called SmartMaps, representing spatially continuous values of local built environment variables in the domains of neighborhood composition, utilitarian destinations, transportation infrastructure, and traffic conditions. Using bootstrap sampling, CIs were estimated for differences in built environment values for home (1666 m) GPS locations. Results: Home and nonhome built environment values were significantly different for more than 90% of variables across subjects (p < 0.001). Only 51% of subjects had higher counts of supermarkets near than away from home. Different measures of neighborhood parks yielded varying results. Conclusions: SmartMaps helped measure local built environment characteristics for a large set of GPS locations. Most subjects had significantly different home and nonhome built environment exposures. Considering the full range of individuals' environmental exposures may improve understanding of effects of the built environment on behavior and health outcomes. (Am J Prev Med 2012;42(4):411-417) (C) 2012 American Journal of Preventive Medicine

Keywords

Built Environment; Public Health Research; Individual Differences; Neighborhoods; Environmental Exposure; Health Of Homeless People; Global Positioning System; Data Analysis; Quantitative Research; Seattle (wash.); Washington (state); Geographic Information-systems; Global Positioning Systems; Physical-activity; Health Research; Urban Form; Land-use; Associations; Transportation; Availability; Walkability

Relation between Higher Physical Activity and Public Transit Use

Saelens, Brian E.; Moudon, Anne Vernez; Kang, Bumjoon; Hurvitz, Philip M.; Zhou, Chuan. (2014). Relation between Higher Physical Activity and Public Transit Use. American Journal Of Public Health, 104(5), 854 – 859.

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Abstract

Objectives. We isolated physical activity attributable to transit use to examine issues of substitution between types of physical activity and potential confounding of transit-related walking with other walking. Methods. Physical activity and transit use data were collected in 2008 to 2009 from 693 Travel Assessment and Community study participants from King County, Washington, equipped with an accelerometer, a portable Global Positioning System, and a 7-day travel log. Physical activity was classified into transit-and non-transit-related walking and nonwalking time. Analyses compared physical activity by type between transit users and nonusers, between less and more frequent transit users, and between transit and nontransit days for transit users. Results. Transit users had more daily overall physical activity and more total walking than did nontransit users but did not differ on either non-transit-related walking or nonwalking physical activity. Most frequent transit users had more walking time than least frequent transit users. Higher physical activity levels for transit users were observed only on transit days, with 14.6 minutes (12.4 minutes when adjusted for demographics) of daily physical activity directly linked with transit use. Conclusions. Because transit use was directly related to higher physical activity, future research should examine whether substantive increases in transit access and use lead to more physical activity and related health improvements.

Keywords

Transportation; Analysis Of Covariance; Analysis Of Variance; Chi-squared Test; Comparative Studies; Confidence Intervals; Geographic Information Systems; Research Funding; Statistics; Walking; Data Analysis; Accelerometry; Cross-sectional Method; Exercise Intensity; Physical Activity; Diary (literary Form); Descriptive Statistics; Washington (state); Work; Car; Impact

Comparing Associations between the Built Environment and Walking in Rural Small Towns and a Large Metropolitan Area

Stewart, Orion T.; Moudon, Anne Vernez; Saelens, Brian E.; Lee, Chanam; Kang, Bumjoon; Doescher, Mark P. (2016). Comparing Associations between the Built Environment and Walking in Rural Small Towns and a Large Metropolitan Area. Environment And Behavior, 48(1), 13 – 36.

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Abstract

The association between the built environment (BE) and walking has been studied extensively in urban areas, yet little is known whether the same associations hold for smaller, rural towns. This analysis examined objective measures of the BE around participants' residence and their utilitarian and recreational walking from two studies, one in the urban Seattle area (n = 464) and the other in nine small U.S. towns (n = 299). After adjusting for sociodemographics, small town residents walked less for utilitarian purposes but more for recreational purposes. These differences were largely explained by differential associations of the BE on walking in the two settings. In Seattle, the number of neighborhood restaurants was positively associated with utilitarian walking, but in small towns, the association was negative. In small towns, perception of slow traffic on nearby streets was positively associated with recreational walking, but not in Seattle. These observations suggest that urban-rural context matters when planning BE interventions to support walking.

Keywords

Physical-activity; Utilitarian Walking; Transportation; Obesity; Adults; Travel; Urban; Prevalence; Strategies; Physical Activity; Walkability; City Planning; Urban Design; Community Health; Gis (geographic Information System); Gps (global Positioning System); Accelerometer; Effect Modification

Beyond the Bus Stop: Where Transit Users Walk

Eisenberg-Guyot, Jerzy; Moudon, Anne V.; Hurvitz, Philip M.; Mooney, Stephen J.; Whitlock, Kathryn B.; Saelens, Brian E. (2019). Beyond the Bus Stop: Where Transit Users Walk. Journal Of Transport & Health, 14.

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Abstract

Objectives: Extending the health benefits of public-transit investment requires understanding how transit use affects pedestrian activity, including pedestrian activity not directly temporally or spatially related to transit use. In this study, we identified where transit users walked on transit days compared with non-transit days within and beyond 400 m and 800 m buffers surrounding their home and work addresses. Methods: We used data collected from 2008 to 2013 in King County, Washington, from 221 non-physically-disabled adult transit users, who were equipped with an accelerometer, global positioning system (GPS), and travel diary. We assigned walking activity to the following buffer locations: less than and at least 400 m or 800 m from home, work, or home/work (the home and work buffers comprised the latter buffer). We used Poisson generalized estimating equations to estimate differences in minutes per day of total walking and minutes per day of non-transit-related walking on transit days compared with non-transit days in each location. Results: We found that durations of total walking and non-transit-related walking were greater on transit days than on non-transit days in all locations studied. When considering the home neighborhood in isolation, most of the greater duration of walking occurred beyond the home neighborhood at both 400 m and 800 m; results were similar when considering the work neighborhood in isolation. When considering the neighborhoods jointly (i.e., by using the home/work buffer), at 400 m, most of the greater duration of walking occurred beyond the home/work neighborhood. However, at 800 m, most of the greater duration of walking occurred within the home/work neighborhood. Conclusions: Transit days were associated with greater durations of total walking and non-transit related walking within and beyond the home and work neighborhoods. Accordingly, research, design, and policy strategies focused on transit use and pedestrian activity should consider locations outside the home and work neighborhoods, in addition to locations within them.

Keywords

Physical-activity; Public-transit; Accelerometer Data; Combining Gps; United-states; Travel; Transportation; Health; Time; Neighborhood

How Does Ride-Hailing Influence Individual Mode Choice? An Examination Using Longitudinal Trip Data from the Seattle Region

Wang, Yiyuan; Moudon, Anne Vernez; Shen, Qing. (2022). How Does Ride-Hailing Influence Individual Mode Choice? An Examination Using Longitudinal Trip Data from the Seattle Region. Transportation Research Record, 2676(3), 621 – 633.

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Abstract

This study investigates the impacts of ride-hailing, which we define as mobility services consisting of both conventional taxis and app-based services offered by transportation network companies, on individual mode choice. We examine whether ride-hailing substitutes for or complements travel by driving, public transit, or walking and biking. The study overcomes some of the limitations of convenience samples or cross-sectional surveys used in past research by employing a longitudinal dataset of individual travel behavior and socio-demographic information. The data include three waves of travel log data collected between 2012 and 2018 in transit-rich areas of the Seattle region. We conducted individual-level panel data modeling, estimating independently pooled models and fixed-effect models of average daily trip count and duration for each mode, while controlling for various factors that affect travel behavior. The results provide evidence of substitution effects of ride-hailing on driving. We found that cross-sectionally, participants who used more ride-hailing tended to drive less, and that longitudinally, an increase in ride-hailing usage was associated with fewer driving trips. No significant associations were found between ride-hailing and public transit usage or walking and biking. Based on detailed travel data of a large population in a major U.S. metropolitan area, the study highlights the value of collecting and analyzing longitudinal data to understand the impacts of new mobility services.

Keywords

Shared Mobility; Ride-hailing; Longitudinal Data; Substitution Between Travel Modes; Complementarity Between Travel Modes; Services; Uber