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.

A Neighborhood Wealth Metric for Use in Health Studies

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

Environments Perceived as Obesogenic Have Lower Residential Property Values

Drewnowski, Adam; Aggarwal, Anju; Rehm, Colin D.; Cohen-Cline, Hannah; Hurvitz, Philip M.; Moudon, Anne V. (2014). Environments Perceived as Obesogenic Have Lower Residential Property Values. American Journal Of Preventive Medicine, 47(3), 260 – 274.

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Abstract

Background: Studies have tried to link obesity rates and physical activity with multiple aspects of the built environment. Purpose: To determine the relation between residential property values and multiple perceived (self-reported) measures of the obesogenic environment. Methods: The Seattle Obesity Study (SOS) used a telephone survey of a representative, geographically distributed sample Of 2,001 King County adults, collected in 2008-2009 and analyzed in 2012-2013. Home addresses were geocoded. Residential property values at the tax parcel level were obtained from the King County tax assessor. Mean residential property values within a 10-minute walk (833-m buffer) were calculated for each respondent. Data on multiple perceived measures of the obesogenic environment were collected by self-report. Correlations and multi-variable linear regression analyses, stratified by residential density, were used to examine the associations among perceived environmental measures, property values, and BMI. Results: Perceived measures of the environment such as crime, heavy traffic, and proximity to bars, liquor stores, and fast food were all associated with lower property values. By contrast, living in neighborhoods that were perceived as safe, quiet, clean, and attractive was associated with higher property values. Higher property values were associated, in turn, with lower BMIs among women. The observed associations between perceived environment measures and BMI were largely attenuated after accounting for residential property values. Conclusions: Environments perceived as obesogenic are associated with lower property values. Studies in additional locations need to explore to what extent other perceived environment measures can be reflected in residential property values. (C) 2014 American Journal of Preventive Medicine

Keywords

Body-mass Index; Physical-activity; Objective Measures; Childhood Obesity; Food Stores; Neighborhood Disorder; Atherosclerosis Risk; Collective Efficacy; Racial Composition; Built Environment

Obesity, Diet Quality, Physical Activity, and the Built Environment: The Need for Behavioral Pathways

Drewnowski, Adam; Aggarwal, Anju; Tang, Wesley; Hurvitz, Philip M.; Scully, Jason; Stewart, Orion; Moudon, Anne Vernez. (2016). Obesity, Diet Quality, Physical Activity, and the Built Environment: The Need for Behavioral Pathways. BMC Public Health, 16.

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Abstract

Background: The built environment ( BE) is said to influence local obesity rates. Few studies have explored causal pathways between home-neighborhood BE variables and health outcomes such as obesity. Such pathways are likely to involve both physical activity and diet. Methods: The Seattle Obesity Study ( SOS II) was a longitudinal cohort of 440 adult residents of King Co, WA. Home addresses were geocoded. Home-neighborhood BE measures were framed as counts and densities of food sources and physical activity locations. Tax parcel property values were obtained from County tax assessor. Healthy Eating Index ( HEI 2010) scores were constructed using data from food frequency questionnaires. Physical activity ( PA) was obtained by self-report. Weights and heights were measured at baseline and following 12 months' exposure. Multivariable regressions examined the associations among BE measures at baseline, health behaviors ( HEI-2010 and physical activity) at baseline, and health outcome both cross-sectionally and longitudinally. Results: None of the conventional neighborhood BE metrics were associated either with diet quality, or with meeting PA guidelines. Only higher property values did predict better diets and more physical activity. Better diets and more physical activity were associated with lower obesity prevalence at baseline and 12 mo, but did not predict weight change. Conclusion: Any links between the BE and health outcomes critically depend on establishing appropriate behavioral pathways. In this study, home-centric BE measures, were not related to physical activity or to diet. Further studies will need to consider a broader range of BE attributes that may be related to diets and health.

Keywords

Body-mass Index; Local Food Environment; Residential Property-values; Supermarket Accessibility; Park Proximity; Neighborhood Walkability; Vegetable Consumption; Atherosclerosis Risk; Restaurant Food; Associations; Built Environment; Physical Activity; Obesity; Diet Quality

Why Neighborhood Park Proximity Is Not Associated with Total Physical Activity

Stewart, Orion T.; Moudon, Anne Vernez; Littman, Alyson J.; Seto, Edmund; Saelens, Brian E. (2018). Why Neighborhood Park Proximity Is Not Associated with Total Physical Activity. Health & Place, 52, 163 – 169.

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Abstract

This study explored how parks within the home neighborhood contribute to total physical activity (PA) by isolating park-related PA. Seattle-area adults (n = 634) were observed using time-matched accelerometer, Global Positioning System (GPS), and travel diary instruments. Of the average 42.3 min of daily total PA, only 11% was related to parks. Both home neighborhood park count and area were associated with park-based PA, but not with PA that occurred elsewhere, which comprised 89% of total PA. This study demonstrates clear benefits of neighborhood parks for contributing to park-based PA while helping explain why proximity to parks is rarely associated with overall PA.

Keywords

Physical Activity; Parks; Urban Planning; Environmental Health; Global Positioning System; Built Environment; Green Space; Recreation; Social Determinants Of Health; Health Research; Accelerometer Data; Self-selection; United-states; Public Parks; Older Women; Walking; Adults; Facilities

Differential Associations of the Built Environment on Weight Gain by Sex and Race/Ethnicity but Not Age

Buszkiewicz, James H.; Bobb, Jennifer F.; Kapos, Flavia; Hurvitz, Philip M.; Arterburn, David; Moudon, Anne Vernez; Cook, Andrea; Mooney, Stephen J.; Cruz, Maricela; Gupta, Shilpi; Lozano, Paula; Rosenberg, Dori E.; Theis, Mary Kay; Anau, Jane; Drewnowski, Adam. (2021). Differential Associations of the Built Environment on Weight Gain by Sex and Race/Ethnicity but Not Age. International Journal Of Obesity, 45(12), 2648 – 2656.

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Abstract

Objective To explore the built environment (BE) and weight change relationship by age, sex, and racial/ethnic subgroups in adults. Methods Weight trajectories were estimated using electronic health records for 115,260 insured Kaiser Permanente Washington members age 18-64 years. Member home addresses were geocoded using ArcGIS. Population, residential, and road intersection densities and counts of area supermarkets and fast food restaurants were measured with SmartMaps (800 and 5000-meter buffers) and categorized into tertiles. Linear mixed-effect models tested whether associations between BE features and weight gain at 1, 3, and 5 years differed by age, sex, and race/ethnicity, adjusting for demographics, baseline weight, and residential property values. Results Denser urban form and greater availability of supermarkets and fast food restaurants were associated with differential weight change across sex and race/ethnicity. At 5 years, the mean difference in weight change comparing the 3rd versus 1st tertile of residential density was significantly different between males (-0.49 kg, 95% CI: -0.68, -0.30) and females (-0.17 kg, 95% CI: -0.33, -0.01) (P-value for interaction = 0.011). Across race/ethnicity, the mean difference in weight change at 5 years for residential density was significantly different among non-Hispanic (NH) Whites (-0.47 kg, 95% CI: -0.61, -0.32), NH Blacks (-0.86 kg, 95% CI: -1.37, -0.36), Hispanics (0.10 kg, 95% CI: -0.46, 0.65), and NH Asians (0.44 kg, 95% CI: 0.10, 0.78) (P-value for interaction <0.001). These findings were consistent for other BE measures. Conclusion The relationship between the built environment and weight change differs across demographic groups. Careful consideration of demographic differences in associations of BE and weight trajectories is warranted for investigating etiological mechanisms and guiding intervention development.

Keywords

Body-mass Index; Socioeconomic-status; Food Environment; Obesity; Health; Outcomes; Scale; Risk; Minority & Ethnic Groups; Urban Environments; Etiology; Demographics; Sex; Residential Density; Supermarkets; Age; Race; Ethnicity; Property Values; Body Weight Gain; Electronic Medical Records; Fast Food; Electronic Health Records; Real Estate; Subgroups; Demography; Trajectory Analysis; Weight