Area-level socioeconomic inequalities in overweight and obesity: Systematic review on moderation by built-environment attributes

Selvakumaran, Sungkavi; Lin, Chien-Yu; Hadgraft, Nyssa; Chandrabose, Manoj; Owen, Neville; Sugiyama, Takemi

doi:10.1016/j.healthplace.2023.103101

Cited by 2 publications

(2 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To make matters worse, health status and healthy lifestyles are interactively or independently associated with neighborhood socioeconomic and built environment characteristics [3]. Socioeconomic gradients in the built environment features that create a supportive physical and social context for health-related behaviours have been frequently emphasized [4,5], and numerous cases have highlighted that the prevalence of obesity and NCDs are much higher in deprived neighborhoods with higher concentrations of low-income and ethnic minorities groups [6][7][8]. In this regard, unraveling associations between neighborhood deprivation and the built environment should provide in-depth insights into strategies for urban planners and policymakers.…”

Section: Introductionmentioning

confidence: 99%

“…Walking has been widely acknowledged to deliver health benefits in diverse ways [7,9]. Studies have evidenced that a walkable environment plays an essential role in combating obesity and non-communicable diseases [5,10]. Creating a walkable environment is not only a practical step toward the UN Sustainable Development Goals (SDGs) 2030 [11], but also a useful strategy recommended by the New Urbanism for reshaping urban areas [7,12,13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unraveling the Dynamic Relationship between Neighborhood Deprivation and Walkability over Time: A Machine Learning Approach

Wang,

Li,

Weng

2024

Land

View full text Add to dashboard Cite

Creating a walkable environment is an essential step toward the 2030 Sustainable Development Goals. Nevertheless, not all people can enjoy a walkable environment, and neighborhoods with different socioeconomic status are found to vary greatly with walkability. Former studies have typically unraveled the relationship between neighborhood deprivation and walkability from a temporally static perspective and the produced estimations to a point-in-time snapshot were believed to incorporate great uncertainties. The ways in which neighborhood walkability changes over time in association with deprivation remain unclear. Using the case of the Hangzhou metropolitan area, we first measured the neighborhood walkability from 2016 to 2018 by calculating a set of revised walk scores. Further, we applied a machine learning algorithm, the kernel-based regularized least squares regression in particular, to unravel how neighborhood walkability changes in relation to deprivation over time. The results not only capture the nonlinearity in the relationship between neighborhood deprivation and walkability over time, but also highlight the marginal effects of each neighborhood deprivation indicator. Additionally, comparisons of the outputs between the machine learning algorithm and OLS regression illustrated that the machine learning approach did tell a different story and should contribute to remedying the contradictory conclusions in earlier studies. This paper is believed to renew the understanding of social inequalities in walkability by bringing the significance of temporal dynamics and structural interdependences to the fore.

show abstract