Influential factors of intercity patient mobility and its network structure in China

Intercity patient mobility reflects the geographic mismatch between healthcare resources and the population, and has rarely been studied with big data at large spatial scales. In this paper, the authors investigated the patterns of intercity patient mobility and factors influencing this behavior based on >4 million hospitalization records of patients with chronic kidney disease in China. To provide practical policy recommendations, a role identification framework informed by complex network theory was proposed considering the strength and distribution of connections of cities in mobility networks. Such a mobility network features multiscale community structure with “universal administrative constraints and a few boundary breaches”. The authors discovered that cross-module visits which accounted for only 20 % of total visits, accounted for >50 % of the total travel distance. The explainable machine learning modeling results revealed that distance has a power-law-like effect on flow volume, and high-quality healthcare resources are an important driving factor. This paper provides not only a methodological reference for patient mobility studies but also valuable insights into public health policies.

• Record URL:
  https://doi.org/10.1016/j.cities.2022.103975
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0264275122004140
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/02642751
• Supplemental Notes:
  • © 2022 The Authors. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Ding, Jiaqi
  • Yang, Chao
  • Wang, Yueyao
  • Li, Pengfei
  • Wang, Fulin
  • Kang, Yuhao
  • Wang, Haoyang
  • Liang, Ze
  • Zhang, Jiawei
  • Han, Peien
  • Wang, Zheng
  • Chu, Erxuan
  • Li, Shuangcheng
  • Zhang, Luxia
• Publication Date: 2023-1

Language

• English

Media Info

• Media Type: Digital/other
• Features: Appendices; Figures; Maps; References; Tables;
• Pagination: 103975
• Serial:
  • Cities
  • Volume: 132
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0264-2751
  • Serial URL: http://www.sciencedirect.com/science/journal/02642751

Subject/Index Terms

• TRT Terms: Intercity travel; Machine learning; Mobility; Networks; Patients; Trip length
• Geographic Terms: China
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01865186
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 28 2022 10:56AM