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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Decoding the association between health level and human settlements environment: a machine learning-driven provincial

Haidong Zhu1, Xiaoqing Peng1

  • 1Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, Hong Kong SAR, China.

Frontiers in Public Health
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Summary

Rapid urbanization in China impacts public health, with optimal health linked to synergistic development of infrastructure, digital access, and services. Coordinated urban planning is crucial for health-oriented policies.

Keywords:
Shapley additive explanationsXGBoosthealth levelhuman settlement environmentmachine learning

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Area of Science:

  • Environmental Health
  • Urban Planning
  • Public Health Policy

Background:

  • Rapid urbanization in China significantly alters the human settlement environment (HSE), presenting both public health opportunities and challenges.
  • Existing research often lacks a comprehensive, macro-level understanding of the HSE-health nexus, focusing on micro-level contexts, single dimensions, or specific diseases.

Purpose of the Study:

  • To assess associations between population health and multidimensional HSE features at the provincial level in China.
  • To uncover nonlinear relationships and interaction effects between HSE and population health level.

Main Methods:

  • Constructed a composite Health Level Index (HLI) using Entropy-TOPSIS from 2012-2022 panel data across 31 Chinese provinces.
  • Employed XGBoost machine learning to model HSE-HLI relationships, utilizing SHAP values and Partial Dependence Plots (PDPs) for interpretation.

Main Results:

  • XGBoost demonstrated strong predictive capacity, outperforming benchmark models.
  • Key influential HSE features identified: 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).
  • Nonlinear relationships and threshold effects were observed; optimal health correlated with high UR, MPPR, RAPC, and moderate NMIB, emphasizing synergistic development.

Conclusions:

  • The HSE-health relationship is nonlinear, multidimensional, and interactive.
  • Effective urban health governance necessitates coordinated development of urbanization, digital infrastructure, public services, and rational healthcare resource allocation.
  • Findings provide actionable insights for health-oriented urban planning and policy in rapidly urbanizing regions.