| Title | Influential factors of intercity patient mobility and its network structure in China |
| 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 |
| Affiliation | Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China Peking Univ, Key Lab Earth Surface Proc, Minist Educ, Beijing 100871, Peoples R China Peking Univ, Peking Univ First Hosp, Dept Med, Renal Div,Inst Nephrol, Beijing 100034, Peoples R China Chinese Acad Med Sci, Res Units Diag & Treatment Immune mediated Kidney, Beijing 100034, Peoples R China Peking Univ, Adv Inst Informat Technol, Hangzhou 311215, Peoples R China Peking Univ, Inst Med Technol, Hlth Sci Ctr, Beijing 100191, Peoples R China Peking Univ, Natl Inst Hlth Data Sci, Beijing 100191, Peoples R China Peking Univ First Hosp, Beijing 100034, Peoples R China Univ Wisconsin, Dept Geog, GeoDS Lab, Madison, WI 53705 USA Univ New South Wales, Sch Math & Stat, Sydney, NSW 2052, Australia Peking Univ, Sch Publ Hlth, Dept Hlth Policy & Management, Beijing 100191, Peoples R China Beijing Normal Univ, Fac Geog Sci, Sch Geog, Beijing 100875, Peoples R China |
| Keywords | HEALTH-CARE EUROPEAN-UNION TRAVEL SERVICES DISEASE CITIES MAP |
| Issue Date | Jan-2023 |
| Publisher | CITIES |
| Abstract | 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, we 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". We 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 highquality 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. |
| URI | http://hdl.handle.net/20.500.11897/655613 |
| ISSN | 0264-2751 |
| DOI | 10.1016/j.cities.2022.103975 |
| Indexed | SSCI |
| Appears in Collections: | 城市与环境学院 地表过程分析与模拟教育部重点实验室 第一医院 公共卫生学院 |
