Decoding the association between health level and human settlements environment

A machine learning-driven provincial analysis in China

Dados Bibliográficos

ID22081600
AutoresHaidong Zhu (0000-0002-8384-0464), Hai‐Dong Zhu (0000-0003-1798-7641, City University of Hong Kong), Xiaoqing Peng (0000-0002-2285-5611, City University of Hong Kong, autor correspondente)
Ano2025
Volume13
Páginas1672479-1672479
Data de publicação2025-09-03
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoFrontiers in Public Health (JOURNAL)
Identificadores do periódicoISSN: 2296-2565 • E-ISSN: 2296-2565
EditoraFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1672479
PMID40969648
OpenAlexW4413961649
IdiomaEN
Referências citadas62

Background: 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. Objective: 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. Methods: Using panel data from 31 Chinese provinces spanning 2012 to 2022, a composite Health Level Index (HLI) was constructed based on four core health indicators using the Entropy-TOPSIS method. 19 HSE indicators covering five dimensions-ecological environment, living environment, infrastructure, public services, and sustainable environment-were selected as explanatory variables. The study employed the XGBoost machine learning algorithm to model the relationship between HSE and HLI. SHAP values and Partial Dependence Plots (PDPs) were used to interpret feature importance, nonlinear relationships, threshold values, and interaction effects. Results: XGBoost outperformed all benchmark models, confirming its strong predictive capacity. SHAP analysis identified six key features-number of medical institution beds (NMIB), urbanization rate (UR), mobile phone penetration rate (MPPR), road area per capita (RAPC), population density (PD), and urban gas penetration rate (UGPR)-as the most influential factors. Nonlinear relationships and threshold effects were observed between key features and population health level. PDP plots further revealed that optimal health levels are typically associated with high UR, high MPPR, high RAPC, and moderate NMIB, underscoring the importance of structural synergy over isolated infrastructure expansion. Conclusion: This study provides robust evidence that the relationship between HSE and health is nonlinear, multidimensional, and highly interactive. Effective urban health governance requires coordinated development of urbanization, digital infrastructure, and public services, along with rational healthcare resource allocation. The findings offer actionable insights for health-oriented urban planning and policy formulation in rapidly urbanizing regions

China · Data science · Decoding methods · Environmental health · Geography · Human health · Human settlement · Telecommunications · Air Quality and Health Impacts · Computer Science · Health, Environment, Cognitive Aging · Medicine · Psychology · Urban Transport and Accessibility