AUTHOR=Sun Yue , Lu Wei , Gu Jinyuan , Yao Yishu , Wan Tianyue 

TITLE=Unveiling the obesogenic neighborhood food environment factors and typologies in Tianjin, China: an integrative analysis of perceived and objective measures

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1665021

DOI=10.3389/fpubh.2025.1665021

ISSN=2296-2565

ABSTRACT=IntroductionAssessing and intervening in food environments constitutes a critical strategy for addressing the obesity epidemics. However, existing assessments predominantly focus on either objective or perceived dimensions, with limited attention to developing countries. This study investigates the impact of neighborhood-level food environments on resident obesity in a national central city of China and establishes a typology of obesogenic community profiles.MethodsWe developed an integrative tool that harmonizes objective geospatial data with subjective perceptual metrics. Leveraging stratified sampling survey data on neighborhood food environments (N = 405) and multiscale geospatial datasets from Tianjin, China (2023), we establish a comprehensive indicator repository for neighborhood food environments. Dimensionality reduction via principal component analysis (PCA) was applied to all measured indicators, followed by an ordinal multinomial regression model to identify significant obesogenic determinants at the neighborhood level. Finally, the K-means clustering algorithm was subsequently implemented to delineate prototypical obesogenic neighborhood typologies.ResultsAmong 10 principal components derived from PCA, four obesogenic factors were identified, ranked by effect magnitude: FAC_8 (Perceived Community Food Accessibility Index, β = −0.382, p = 0.001, OR = 0.68), FAC_4 (Food Availability and Diversity within 500-1000m, β = 0.225, p = 0.061, OR = 1.25), FAC_6 (Unhealthy Dietary Behavior, β = −0.191, p = 0.066, OR = 0.68), and FAC_3 (Retail Food Environment Index within 500m, β = −0.184, p = 0.078, OR = 0.83). K-means clustering delineated three obesogenic neighborhood types: Objective Deprived (N = 10, 6.1%), Objective Overloaded (N = 37, 22.56%), and Objective Overloaded-Dietary Behavior Integrated (N = 117, 71.34%).DiscussionThis study revealed that within the context of China’s urban built environment, the prevalence of “food deserts” is minimal. Conversely, an augmented proportion of widely recognized healthy food facilities in developed Western countries has been observed to heighten the risk of obesity, including supermarkets and fresh food markets. This phenomenon exhibits a scale-dependence, indicating that its impact increases with the magnitude of the scale. The most salient characteristic of obesogenic neighborhoods in China is their high objective environmental risk. The study examined and identified neighborhood-level obesity factors and provided a generalizable method for identifying obesogenic neighborhood types, thereby providing empirical evidence for obesity research in developing countries.