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Contextualizing Walkability: Do Relationships Between Built Environments and Walking Vary by Socioeconomic Context?

ABSTRACT: Problem research strategy and findings:Supportive built environments for walking are linked to higher rates of walking and physical activity, but little is known about this relationship for socioeconomically disadvantaged (e.g., low-income and racial/ethnic minority) populations. We review 17 articles and find that most show that the built environment has weaker effects on walking and physical activity for disadvantaged than advantaged groups. Those who lived in supportive built environments walked more and were more physically active than those who did not, but the effect was about twice as large for advantaged groups. We see this difference because disadvantaged groups walked more in unsupportive built environments and less in supportive built environments, though the latter appears more influential. Takeaway for practice:Defining walkability entirely in built environment terms may fail to account for important social and individual/household characteristics and other non-built environment factors that challenge disadvantaged groups, including fear of crime and lack of social support. Planners must be sensitive to these findings and to community concerns about gentrification and displacement in the face of planned built environment improvements that may benefit more advantaged populations. We recommend five planning responses: Recognize that the effects of the built environment may vary by socioeconomics; use holistic approaches to improve walkability; expand walkability definitions to address a range of social and physical barriers; partner across agencies, disciplines, and professions; and evaluate interventions in different socioeconomic environments.

SUBMITTER: Adkins A

PROVIDER: S-EPMC6873812 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Contextualizing Walkability: Do Relationships Between Built Environments and Walking Vary by Socioeconomic Context?

Adkins Arlie A Makarewicz Carrie C Scanze Michele M Ingram Maia M Luhr Gretchen G

Journal of the American Planning Association. American Planning Association 20170712 3

<h4>Problem research strategy and findings</h4>Supportive built environments for walking are linked to higher rates of walking and physical activity, but little is known about this relationship for socioeconomically disadvantaged (e.g., low-income and racial/ethnic minority) populations. We review 17 articles and find that most show that the built environment has weaker effects on walking and physical activity for disadvantaged than advantaged groups. Those who lived in supportive built environm ...[more]

PMID: 31762526

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Project description:BackgroundAlthough socioeconomic status (SES) has been shown to modify associations between the neighborhood built environment and physical activity, contradictory results exist. Objectives of this cross-sectional and longitudinal analysis were to: 1) examine whether overall neighborhood walkability and specific built characteristics were associated with walking among adults at a single point in time and after they relocate neighborhoods, and 2) test for effect modification of these associations by SES.MethodsWe linked longitudinal data from 703 adults who relocated urban neighborhoods between two waves of Alberta's Tomorrow Project (2008-2015) to neighborhood built environment data. We created a walkability index from measures of population counts, street connectivity, and destination diversity within 400 m of participants' homes. In cross-sectional analyses, we used generalized linear models to estimate associations between built characteristics and minutes walked per week at baseline. For the longitudinal analyses, we used fixed-effects linear regression models to estimate associations between changes in built characteristics and minutes walked per week. We also assessed if indicators of SES (individual education or household income) modified both sets of associations.ResultsMost cross-sectional and longitudinal associations were small and statistically non-significant. Neighborhood population count (b = 0.03, 95% CI: 0.01, 0.07) and street connectivity (b = - 1.75, 95% CI: - 3.26, - 0.24) were cross-sectionally associated with walking duration among the overall sample. None of the longitudinal associations were statistically significant among the overall sample. There was limited evidence of effect modification by SES, however, we found negative cross-sectional associations between street connectivity and walking among adults with lower education and income, and a positive association between percent change in walkability and change in walking among lower educated adults.ConclusionsDespite population count and street connectivity being associated with walking at baseline, changes in these built environment variables were not associated with changes in walking following residential relocation. Our findings also provide evidence, albeit weak, that changes in neighborhood walkability, resulting from residential relocation, might more strongly affect walking among low SES adults. Further longitudinal research is needed to examine built environment characteristics with walking for different purposes and to test for inequitable socioeconomic impacts.

Project description:BackgroundInter-relationships among built and socioeconomic environmental characteristics may result in confounding of associations between environment exposure measures and health behaviors or outcomes, but traditional multivariate adjustment can be inappropriate due to collinearity.MethodsWe used principal factor analysis to describe inter-relationships between a large set of Geographic Information System-derived built and socioeconomic environment measures for adolescents in the National Longitudinal Study of Adolescent Health (Wave I, 1995-96, n = 17,294). Using resulting factors in sex-stratified multivariate negative binomial regression models, we tested for confounding of associations between built and socioeconomic environment characteristics and moderate to vigorous physical activity (MVPA). Finally, we used knowledge gained from factor analysis to construct replicable environmental measures that account for inter-relationships and avoid collinearity.ResultsUsing factor analysis, we identified three built environment constructs [(1) homogenous landscape; 2) development intensity with high pay facility count; 3) development intensity with high public facility count] and two socioeconomic environment constructs [1) advantageous economic environment, 2) disadvantageous social environment]. In regression analysis, confounding of built environment-MVPA associations by socioeconomic environment factors was stronger than among built environment factors. In fully adjusted models, MVPA was negatively associated with the highest (versus lowest) quartile of homogenous land cover in males [exp(coeff) (95% CI): 0.91 (0.86, 0.96)] and intensity (pay facilities) [exp(coeff) (95% CI): 0.92 (0.85, 0.99)] in females. Single proxy measures (Simpson's diversity index, count of pay facilities, count of public facilities, median household income, and crime rate) representing each environmental construct replicated associations with MVPA.ConclusionsEnvironmental characteristics are inter-related. Both built and SES environments should be incorporated into analysis in order to minimize confounding. Single environmental measures may be useful proxies for environmental constructs in longitudinal analysis and replication in external populations, but more research is needed to better understand mechanisms of action, and ultimately identify policy-relevant environmental determinants of physical activity.

Project description:Background and objectivesUnderstanding the social determinants of health is a major goal in evolutionary biology and human health research. Low socioeconomic status (often operationalized as absolute material wealth) is consistently associated with chronic stress, poor health and premature death in high-income countries. However, the degree to which wealth gradients in health are universal-or are instead made even steeper under contemporary, post-industrial conditions-remains poorly understood.MethodologyWe quantified absolute material wealth and several health outcomes among a population of traditional pastoralists, the Turkana of northwest Kenya, who are currently transitioning toward a more urban, market-integrated lifestyle. We assessed whether wealth associations with health differed in subsistence-level versus urban contexts. We also explored the causes and consequences of wealth-health associations by measuring serum cortisol, potential sociobehavioral mediators in early life and adulthood, and adult reproductive success (number of surviving offspring).ResultsHigher socioeconomic status and greater material wealth predicts better self-reported health and more offspring in traditional pastoralist Turkana, but worse cardiometabolic health and fewer offspring in urban Turkana. We do not find robust evidence for either direct biological mediators (cortisol) or indirect sociobehavioral mediators (e.g. adult diet or health behaviors, early life experiences) of wealth-health relationships in either context.Conclusions and implicationsWhile social gradients in health are well-established in humans and animals across a variety of socioecological contexts, we show that the relationship between wealth and health can vary within a single population. Our findings emphasize that changes in economic and societal circumstances may directly alter how, why and under what conditions socioeconomic status predicts health.Lay summaryHigh socioeconomic status predicts better health and more offspring in traditional Turkana pastoralists, but worse health and fewer offspring in individuals of the same group living in urban areas. Together, our study shows that under different economic and societal circumstances, wealth effects on health may manifest in very different ways.

Project description:Microbial interactions have profound impacts on biodiversity, biogeochemistry, and ecosystem functioning, and yet, they remain poorly understood in the ocean and with respect to changing environmental conditions. We applied hierarchical clustering of an annual 16S and 18S amplicon dataset in the Skidaway River Estuary, which revealed two similar clusters for prokaryotes (Bacteria and Archaea) and protists: Cluster 1 (March-May and November-February) and Cluster 2 (June-October). We constructed co-occurrence networks from each cluster to explore how microbial networks and relationships vary between environmentally distinct periods in the estuary. Cluster 1 communities were exposed to significantly lower temperature, sunlight, NO3, and SiO4; only NH4 was higher at this time. Several network properties (e.g., edge number, degree, and centrality) were elevated for networks constructed with Cluster 1 vs. 2 samples. There was also evidence that microbial nodes in Cluster 1 were more connected (e.g., higher edge density and lower path length) compared to Cluster 2, though opposite trends were observed when networks considered Prokaryote-Protist edges only. The number of Prokaryote-Prokaryote and Prokaryote-Protist edges increased by >100% in the Cluster 1 network, mainly involving Flavobacteriales, Rhodobacterales, Peridiniales, and Cryptomonadales associated with each other and other microbial groups (e.g., SAR11, Bacillariophyta, and Strombidiida). Several Protist-Protist associations, including Bacillariophyta correlated with Syndiniales (Dino-Groups I and II) and an Unassigned Dinophyceae group, were more prevalent in Cluster 2. Based on the type and sign of associations that increased in Cluster 1, our findings indicate that mutualistic, competitive, or predatory relationships may have been more representative among microbes when conditions were less favorable in the estuary; however, such relationships require further exploration and validation in the field and lab. Coastal networks may also be driven by shifts in the abundance of certain taxonomic or functional groups. Sustained monitoring of microbial communities over environmental gradients, both spatial and temporal, is critical to predict microbial dynamics and biogeochemistry in future marine ecosystems.

Project description:Walkability is considered an important dimension of healthy communities. However, variable associations between measures of walkability and physical activity have been observed, particularly among older persons. Given the challenges older persons may have navigating stairs on walking routes, the presence of stairs may be an explanatory factor for these mixed associations. The purposes of this scoping review were to determine whether studies examining the relationship between walkability and physical activity included items that assessed stairs and what relationships were found.Systematic reviews were identified by entering search terms into five database search engines. Eligibility criteria were: a) published between 2008 and 2017, b) examined the relationship between walkability and physical activity, c) included a focus on persons aged 65 years and older, and d) written in English. The full articles for all primary studies included in eligible systematic reviews were then retrieved. Duplicates were removed. Information about where the study took place, walkability measures used, types of walkability data obtained (objective and/or subjective) and questions asked about stairs were extracted from the full text articles.Eleven systematic reviews were identified; seven were eligible. After removing duplicates, 289 primary studies remained for review. Measures of neighborhood walkability were present in 205 studies; a minority (n?=?5, 2.4%) included items about stairs. No information was obtained on the structural features of the stairs.The presence of stairs may deter older persons (and others) from walking outdoors. Standard measures to document the presence and characteristics of stairs, and sampling approaches to select stairs for assessment are needed. The inclusion of these measures would augment the utility and comparability of studies examining relationships between walkability and physical activity and better inform planning and policy decisions.

Dataset Information

Contextualizing Walkability: Do Relationships Between Built Environments and Walking Vary by Socioeconomic Context?

Publications

Contextualizing Walkability: Do Relationships Between Built Environments and Walking Vary by Socioeconomic Context?

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