<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>13</volume><submitter>Zhu H</submitter><pubmed_abstract>&lt;h4>Background&lt;/h4>Rapid urbanization in China has significantly reshaped the human settlement environment (HSE), bringing opportunities and challenges for public health. While existing studies have explored environmental-health relationships, most are confined to micro-level contexts, focus on single environmental dimensions, or assess specific diseases, thus lacking a comprehensive, macro-level understanding.&lt;h4>Objective&lt;/h4>This study aims to assess the associations between population health level and multidimensional HSE features at the provincial level in China and uncover nonlinear relationships and interaction effects underlying the association between HSE and population health level.&lt;h4>Methods&lt;/h4>Using panel data from 31 Chinese provinces spanning 2012 to 2022, a composite Healt</pubmed_abstract><journal>Frontiers in public health</journal><pagination>1672479</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12440893</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Decoding the association between health level and human settlements environment: a machine learning-driven provincial analysis in China.</pubmed_title><pmcid>PMC12440893</pmcid><pubmed_authors>Peng X</pubmed_authors><pubmed_authors>Zhu H</pubmed_authors></additional><is_claimable>false</is_claimable><name>Decoding the association between health level and human settlements environment: a machine learning-driven provincial analysis in China.</name><description>&lt;h4>Background&lt;/h4>Rapid urbanization in China has significantly reshaped the human settlement environment (HSE), bringing opportunities and challenges for public health. While existing studies have explored environmental-health relationships, most are confined to micro-level contexts, focus on single environmental dimensions, or assess specific diseases, thus lacking a comprehensive, macro-level understanding.&lt;h4>Objective&lt;/h4>This study aims to assess the associations between population health level and multidimensional HSE features at the provincial level in China and uncover nonlinear relationships and interaction effects underlying the association between HSE and population health level.&lt;h4>Methods&lt;/h4>Using panel data from 31 Chinese provinces spanning 2012 to 2022, a composite Healt</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025</publication><modification>2026-04-27T03:21:32.794Z</modification><creation>2026-04-27T03:11:32.873Z</creation></dates><accession>S-EPMC12440893</accession><cross_references><pubmed>40969648</pubmed><doi>10.3389/fpubh.2025.1672479</doi></cross_references></HashMap>