{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T00:38:02Z","timestamp":1792802282495,"version":"4.5.0"},"reference-count":58,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aimed to compute the spatially varying associations between seven chronic health conditions\u2014high blood pressure, asthma, chronic obstructive pulmonary disease (COPD), stroke, cancer, diabetes, and frequent mental distress\u2014and key environmental, built-environment, and socioeconomic determinants at the fine, census-tract scale in Harris County, Texas, USA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The study uses three methods: Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) to develop association models using 2022 data. The study further validates the developed models using data from 2020 to 2024.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      MGWR outperformed both OLS and GWR for every health outcome, increasing the adjusted R\n                      <jats:sup>2<\/jats:sup>\n                      (for example, from 0.57 to 0.75 for blood pressure and from 0.58 to 0.70 for cancer) and reducing the corrected Akaike Information Criterion (AICc) by several hundred points. Poverty and particulate matter (PM\n                      <jats:sub>2.5<\/jats:sub>\n                      ) were the most consistent predictors, with the strongest positive coefficients concentrated in central, eastern, and southeastern tracts near industrial corridors and in lower-income communities. Different determinants operated at distinct spatial scales, and re-estimating the framework for 2020, 2021, 2023, and 2024 reproduced the 2022 patterns (|\u0394R\n                      <jats:sup>2<\/jats:sup>\n                      |\u202f\u2264\u202f0.09), confirming the stability of the models. These fine-resolution results identify specific high-burden neighborhoods that global models may overlook.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>By allowing each determinant to act at its own spatial scale, MGWR produced a transferable framework for identifying vulnerable communities and estimating chronic disease burden where health data are sparse or missing. The fine-resolution, census-tract results can help guide place-based public health interventions toward the eastern and southeastern areas of Harris County where poverty, air pollution, and chronic disease prevalence coincide, supporting more efficient and targeted resource allocation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1790559","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T05:39:39Z","timestamp":1789364379000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Geospatial modeling of environmental and socioeconomic drivers of health outcomes"],"prefix":"10.3389","volume":"14","author":[{"given":"Amirhooman","family":"Yalali","sequence":"first","affiliation":[{"name":"Civil and Environmental Engineering, University of Houston","place":["Houston, TX, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanadi S.","family":"Rifai","sequence":"additional","affiliation":[{"name":"Civil and Environmental Engineering, University of Houston","place":["Houston, TX, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omolola E.","family":"Adepoju","sequence":"additional","affiliation":[{"name":"Health Systems & Population Science, University of Houston","place":["Houston, TX, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1056\/NEJMoa1817364","article-title":"Ambient particulate air pollution and daily mortality in 652 cities","volume":"381","author":"Liu","year":"2019","journal-title":"N Engl J Med"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"2290","DOI":"10.1093\/eurheartj\/ehx263","article-title":"BioSHaRE the study consortium. 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Public Health"},{"key":"ref41","year":"2024"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/s11135-006-9018-6","article-title":"A caution regarding rules of thumb for variance inflation factors","volume":"41","author":"O\u2019brien","year":"2007","journal-title":"Qual Quant"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.apgeog.2008.08.007","article-title":"Urban expansion in Centre County, Pennsylvania: spatial dynamics and landscape transformations","volume":"29","author":"Batisani","year":"2009","journal-title":"Appl Geogr"},{"key":"ref44","first-page":"631","volume-title":"Applied Economics","author":"Bacha","year":"2003"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1177\/0160017607301609","article-title":"Can geographically weighted regressions improve regional analysis and policy making?","volume":"30","author":"Ali","year":"2007","journal-title":"Int Reg Sci Rev"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1017\/S1068280500009485","article-title":"Amenities and rural Appalachia economic growth","volume":"36","author":"Deller","year":"2007","journal-title":"Agricultural and Resource Economics Review"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"1905","DOI":"10.1068\/a301905","article-title":"Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis","volume":"30","author":"Fotheringham","year":"1998","journal-title":"Environ Plan A"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1068\/a3162","article-title":"Statistical tests for spatial nonstationarity based on the geographically weighted regression model","volume":"32","author":"Leung","year":"2000","journal-title":"Environ Plan A"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1006\/juec.1996.0025","article-title":"One hundred fifty years of land values in Chicago: a nonparametric approach","volume":"40","author":"McMillen","year":"1996","journal-title":"J Urban Econ"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/s00477-007-0139-9","article-title":"Quantifying geographic variations in associations between alcohol distribution and violence: a comparison of geographically weighted regression and spatially varying coefficient models","volume":"21","author":"Waller","year":"2007","journal-title":"Stoch Environ Res Risk Assess"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"187","DOI":"10.2747\/1548-1603.41.3.187","article-title":"Understanding population segregation from Landsat ETM+ imagery: a geographically weighted regression approach","volume":"41","author":"Yu","year":"2004","journal-title":"GISci Remote Sens"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1080\/13615930120032617","article-title":"Spatial variations in school performance: a local analysis using geographically weighted regression","volume":"5","author":"Fotheringham","year":"2001","journal-title":"Geogr Environ Model"},{"key":"ref53","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1111\/j.1466-822X.2004.00097.x","article-title":"Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna","volume":"13","author":"Foody","year":"2004","journal-title":"Glob Ecol Biogeogr"},{"key":"ref54","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1111\/j.1466-8238.2006.00250.x","article-title":"Multi-extent analysis of the relationship between pteridophyte species richness and climate","volume":"15","author":"Bickford","year":"2006","journal-title":"Glob Ecol Biogeogr"},{"key":"ref55","article-title":"Statistical analysis of geographic information with ArcView GIS and 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