{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T14:30:57Z","timestamp":1786113057327,"version":"build-2736575974"},"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Objectives<\/jats:title>\n Adult obesity prevalence exhibits significant spatial disparities across England. While traditional global regression models often overlook local variations, it is unclear whether these relationships vary spatially or remain stable. This study aims to robustly identify determinants of clinical obesity and test for spatial non-stationarity using best-practice diagnostics.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n Using 2023\/24 data from 151 Upper Tier Local Authorities, we employed a spatial econometric framework. We specified a\n row-standardized Queen\u2019s contiguity matrix<\/jats:bold>\n for global models. A\n Spatial Error Model (SEM)<\/jats:bold>\n was benchmarked against the\n Spatial Durbin Error Model (SDEM)<\/jats:bold>\n to test for the significance of local spillovers, and against Geographically Weighted Regression (GWR) to test for coefficient non-stationarity.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Obesity prevalence showed strong clustering (Moran\u2019s I\u2009=\u20090.58). The SEM (\n AICc: 806.4<\/jats:bold>\n ) significantly outperformed both OLS (AICc: 891.9) and GWR (AICc: 865.3), effectively eliminating residual spatial autocorrelation (Moran\u2019s I\u2009=\u2009\u22120.03,\n p<\/jats:italic>\n \u2009>\u20090.05). Robustness checks using the SDEM did not significantly improve fit (\n p<\/jats:italic>\n \u2009=\u20090.08). GWR Monte Carlo diagnostics indicated that coefficients for fast-food density and inactivity were spatially stationary (\n p<\/jats:italic>\n \u2009>\u20090.05). Fast-food density exhibited a robust negative association, supporting the \u201curban paradox,\u201d while physical inactivity and low fruit\/vegetable consumption were significant positive drivers.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Contrary to the \u201cone-size-fits-all\u201d critique, the determinants of obesity appear structurally consistent across England. The \u201curban paradox\u201d likely reflects broader urbanization patterns rather than direct causality. Policy should focus on national-level structural interventions addressing deprivation and physical activity.<\/jats:p>\n <\/jats:sec>","DOI":"10.1371\/journal.pone.0353465","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T17:25:47Z","timestamp":1785950747000},"page":"e0353465","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["The urban paradox persists: Spatial stationarity in obesity determinants in post-pandemic England"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5857-5783","authenticated-orcid":true,"given":"Yihan","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"issue":"11","key":"pone.0353465.ref001","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1136\/jech-2020-214401","article-title":"The COVID-19 pandemic and health inequalities","volume":"74","author":"C Bambra","year":"2020","journal-title":"J Epidemiol Community Health"},{"key":"pone.0353465.ref002","unstructured":"Marmot M. 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