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Background <p>Persistent urban-rural disparities in emergency medical service (EMS) demand constitute a core challenge to global public health equity. Existing research focuses predominantly on static comparisons of demand scale, with limited systematic analysis of spatiotemporal dynamics, shock responsiveness, and heterogeneous driving mechanisms across urban and rural EMS systems. Conventional one-size-fits-all regression models further widen the gap between academic findings and on-the-ground EMS operations.</p> Methods <p>Taking Ningbo, China as the case, we defined urban and rural units in full alignment with official EMS response zoning. Using 189,506 validated dispatch records from October 2022 to September 2025 (spanning the exogenous shock of China’s COVID-19 policy adjustment), we applied a sequential framework integrating global negative binomial regression (GNBR) and geographically weighted regression (GWR) to compare spatiotemporal patterns and identify system-specific demand drivers.</p> Results <p>Urban EMS demand showed strong positive spatial autocorrelation (Global Moran’s I = 0.47, <i>p</i> &lt; 0.01) with contiguous high-demand clusters, and exhibited marked volatility across monthly, diurnal, and weekly scales alongside high sensitivity to policy shocks. Rural EMS demand displayed weak spatial autocorrelation (Global Moran’s I = 0.25, <i>p</i> &lt; 0.01) with scattered small-scale agglomeration, and maintained stable temporal patterns decoupled from external disruptions. Driving mechanisms differed structurally: urban demand was driven primarily by population aging and built environment intensity with significant spatial nonstationarity, while rural demand was shaped by traffic accessibility, localized service hubs, and terrain conditions. Based on these findings, we formalize two distinct EMS demand patterns: spatiotemporal coupling in urban areas and spatiotemporal independence in rural areas.</p> Conclusions <p>This study advances theoretical understanding of urban-rural EMS demand heterogeneity beyond superficial scale comparisons. The practice-aligned sequential modeling framework provides a scalable analytical approach for heterogeneous health service research, and delivers evidence for targeted EMS planning, optimized resource allocation, and improved healthcare equity.</p>

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Urban-rural dichotomy of emergency medical service demand: spatiotemporal patterns and heterogeneous driving mechanisms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月3日

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  • Chaowei Wu,
  • Qianqian Yu,
  • Yiji Li,
  • Shiliang Huang,
  • Changlu Li,
  • Li Luo

摘要

Background

Persistent urban-rural disparities in emergency medical service (EMS) demand constitute a core challenge to global public health equity. Existing research focuses predominantly on static comparisons of demand scale, with limited systematic analysis of spatiotemporal dynamics, shock responsiveness, and heterogeneous driving mechanisms across urban and rural EMS systems. Conventional one-size-fits-all regression models further widen the gap between academic findings and on-the-ground EMS operations.

Methods

Taking Ningbo, China as the case, we defined urban and rural units in full alignment with official EMS response zoning. Using 189,506 validated dispatch records from October 2022 to September 2025 (spanning the exogenous shock of China’s COVID-19 policy adjustment), we applied a sequential framework integrating global negative binomial regression (GNBR) and geographically weighted regression (GWR) to compare spatiotemporal patterns and identify system-specific demand drivers.

Results

Urban EMS demand showed strong positive spatial autocorrelation (Global Moran’s I = 0.47, p < 0.01) with contiguous high-demand clusters, and exhibited marked volatility across monthly, diurnal, and weekly scales alongside high sensitivity to policy shocks. Rural EMS demand displayed weak spatial autocorrelation (Global Moran’s I = 0.25, p < 0.01) with scattered small-scale agglomeration, and maintained stable temporal patterns decoupled from external disruptions. Driving mechanisms differed structurally: urban demand was driven primarily by population aging and built environment intensity with significant spatial nonstationarity, while rural demand was shaped by traffic accessibility, localized service hubs, and terrain conditions. Based on these findings, we formalize two distinct EMS demand patterns: spatiotemporal coupling in urban areas and spatiotemporal independence in rural areas.

Conclusions

This study advances theoretical understanding of urban-rural EMS demand heterogeneity beyond superficial scale comparisons. The practice-aligned sequential modeling framework provides a scalable analytical approach for heterogeneous health service research, and delivers evidence for targeted EMS planning, optimized resource allocation, and improved healthcare equity.