An ecosyndemic framework for understanding obesity: Spatial clustering of health, environmental and socioeconomic disadvantage in the Netherlands

INTRODUCTION: Obesity and related health conditions are unevenly distributed across neighborhoods, often co-occuring with multiple health challenges and socioeconomic disadvantages. Using an ecosyndemic framework, which integrates ecological and social dimensions that contribute to the clustering of health problems, this study examines how adverse obesity-related health outcomes spatially cluster in relation to obesogenic environments and socioeconomic position (SEP) across Dutch neighborhoods. METHODS: Nationwide neighborhood-level data on health outcomes, obesogenic environmental exposures (food environment, walkability, drivability, bikeability, sports facilities), and SEP were combined for all inhabited Dutch administrative neighborhoods in 2016 (N = 12,420). Cluster analysis was used to identify distinct neighborhood profiles and descriptive statistics to characterize each cluster, with spatial patterns visualized using an interactive heatmap and principal component plots. RESULTS: Five neighborhood clusters were identified. The 'Ecosyndemic cluster' (N = 1,070 neighborhoods) exhibited the highest burden of obesity (17% [IQR 16;19]), chronic diseases (36% [IQR 33;38%]) and risk of anxiety/depression (55% [IQR 51;58]), unhealthy food environments and low SEP. In contrast, the 'Privileged cluster' (N = 6,425) had more favorable health outcomes and living conditions, including lower obesity prevalence (12% [IQR 11;14]). The 'Psychosocial Vulnerability cluster' (N = 991) was notable for elevated risk of anxiety/depression (47% [IQR 43;51]) combined with relatively low obesity (11% [IQR 8;12]). The 'Syndemic cluster' (N = 1,836; obesity 15% [IQR 14;17]) and 'Towards Privileged cluster' (N = 2,098; obesity 12% [IQR 10;13]) represented intermediate profiles. CONCLUSION: Obesity and related health issues frequently cluster with unfavorable environment and SEP at the neighborhood level. The ecosyndemic framework offers a novel approach for identifying high-risk areas and supports targeted, social and place-based interventions.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2026-08-14
DOI
https://doi.org/10.1371/journal.pone.0354964
Primary Topic
Urban Green Space and Health
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An ecosyndemic framework for understanding obesity: Spatial clustering of health, environmental and socioeconomic disadvantage in the Netherlands

Marco René Spruit, Yvonne T. van der Schouw, Stefan Pichler, Thao Minh Lam et al.
PLoS ONE
Urban Green Space and Health
article

An ecosyndemic framework for understanding obesity: Spatial clustering of health, environmental and socioeconomic disadvantage in the Netherlands

Marco René Spruit, Yvonne T. van der Schouw, Stefan Pichler, Thao Minh Lam, Rimke C. Vos, Jessica C. Kiefte-de Jong, Jona Stunt, Ilonca Vaartjes, Marielle Beenackers, Mirthe Muilwijk, J. Lakerveld
article en

Abstract

INTRODUCTION: Obesity and related health conditions are unevenly distributed across neighborhoods, often co-occuring with multiple health challenges and socioeconomic disadvantages. Using an ecosyndemic framework, which integrates ecological and social dimensions that contribute to the clustering of health problems, this study examines how adverse obesity-related health outcomes spatially cluster in relation to obesogenic environments and socioeconomic position (SEP) across Dutch neighborhoods. METHODS: Nationwide neighborhood-level data on health outcomes, obesogenic environmental exposures (food environment, walkability, drivability, bikeability, sports facilities), and SEP were combined for all inhabited Dutch administrative neighborhoods in 2016 (N = 12,420). Cluster analysis was used to identify distinct neighborhood profiles and descriptive statistics to characterize each cluster, with spatial patterns visualized using an interactive heatmap and principal component plots. RESULTS: Five neighborhood clusters were identified. The 'Ecosyndemic cluster' (N = 1,070 neighborhoods) exhibited the highest burden of obesity (17% [IQR 16;19]), chronic diseases (36% [IQR 33;38%]) and risk of anxiety/depression (55% [IQR 51;58]), unhealthy food environments and low SEP. In contrast, the 'Privileged cluster' (N = 6,425) had more favorable health outcomes and living conditions, including lower obesity prevalence (12% [IQR 11;14]). The 'Psychosocial Vulnerability cluster' (N = 991) was notable for elevated risk of anxiety/depression (47% [IQR 43;51]) combined with relatively low obesity (11% [IQR 8;12]). The 'Syndemic cluster' (N = 1,836; obesity 15% [IQR 14;17]) and 'Towards Privileged cluster' (N = 2,098; obesity 12% [IQR 10;13]) represented intermediate profiles. CONCLUSION: Obesity and related health issues frequently cluster with unfavorable environment and SEP at the neighborhood level. The ecosyndemic framework offers a novel approach for identifying high-risk areas and supports targeted, social and place-based interventions.

PLoS ONEVol. 21(8)
University of Groningen (NL), Utrecht University (NL), Leiden University Medical Center (NL), Erasmus MC (NL), University Medical Center Utrecht (NL), Amsterdam Neuroscience (NL), Vrije Universiteit Amsterdam (NL)
ZonMw, Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Openalex Percentile: Top 84%
Urban Green Space and Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.