Limited education and training, and practical application but high potential demand of spatial epidemiology among public health postgraduates in China: a serial national cross-sectional study

The application of various spatial analysis techniques during the COVID-19 pandemic has improved public health educators and public health postgraduates understanding the role of spatial epidemiology. Yet, the situation and changes in education-and-training, practical application, and potential demand of spatial epidemiology were unclear in China. We aimed to reveal these issues among Chinese public health postgraduates during the early-and-late stages of COVID-19 pandemic. We conducted a serial national cross-sectional study among public health postgraduates at Chinese universities in the early stage (October 2020 to February 2021) and the late stage (November 2022 to April 2023) of COVID-19 pandemic. We collected data by the WeChat-based questionnaire star survey system and compared the differences between the two surveys, using chi-square test for categorical data, and Mann-Whitney U-test for numeric data. Overall, we surveyed 3469 public health postgraduates from 76 universities, and 35.53% of universities offered spatial epidemiology course. Around 9.48% of postgraduates learned spatial epidemiology, and 65.44% hoped universities to deliver spatial epidemiology course. Compared with the early-stage survey, the proportion of universities offering spatial epidemiology courses increased numerically but not significantly (37.14% vs. 27.45%, P = 0.263), while the proportion of postgraduates having learned spatial epidemiology increased significantly (10.40% vs. 8.05%, P = 0.021). The learning degree [1.00 vs. 1.05, P = 0.545] and practical application degree [1.71 vs. 1.91, P = 0.889] still remained at low level. And the potential demand degree remained at relative high level, but decreased significantly [2.50 vs. 3.33, P < 0.001]. The proportion of offering spatial epidemiology course at Chinese universities, and the proportion and degree of learning-and-application of spatial epidemiology among public health postgraduates were low, while the potential demand was high throughout the COVID-19 pandemic. To address these issues and prepare for future pandemics, integrating spatial epidemiology into the compulsory curriculum system of public health education in China would be needed.

Authors

Institutions

Publication Details

Journal
BMC Medical Education
Published
2026-04-23
DOI
https://doi.org/10.1186/s12909-026-09252-1
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Limited education and training, and practical application but high potential demand of spatial epidemiology among public health postgraduates in China: a serial national cross-sectional study

Jun-jie Ye, Yue-Dong Lin, Tao Lan, Xianyan Tang et al.
BMC Medical Education
COVID-19 epidemiological studies
article

Limited education and training, and practical application but high potential demand of spatial epidemiology among public health postgraduates in China: a serial national cross-sectional study

Jun-jie Ye, Yue-Dong Lin, Tao Lan, Xianyan Tang, Man Cheng, Xiao-hong Ma, Xin-Xuan Hu, Qiao Li, Lin Zhao
article en

Abstract

The application of various spatial analysis techniques during the COVID-19 pandemic has improved public health educators and public health postgraduates understanding the role of spatial epidemiology. Yet, the situation and changes in education-and-training, practical application, and potential demand of spatial epidemiology were unclear in China. We aimed to reveal these issues among Chinese public health postgraduates during the early-and-late stages of COVID-19 pandemic. We conducted a serial national cross-sectional study among public health postgraduates at Chinese universities in the early stage (October 2020 to February 2021) and the late stage (November 2022 to April 2023) of COVID-19 pandemic. We collected data by the WeChat-based questionnaire star survey system and compared the differences between the two surveys, using chi-square test for categorical data, and Mann-Whitney U-test for numeric data. Overall, we surveyed 3469 public health postgraduates from 76 universities, and 35.53% of universities offered spatial epidemiology course. Around 9.48% of postgraduates learned spatial epidemiology, and 65.44% hoped universities to deliver spatial epidemiology course. Compared with the early-stage survey, the proportion of universities offering spatial epidemiology courses increased numerically but not significantly (37.14% vs. 27.45%, P = 0.263), while the proportion of postgraduates having learned spatial epidemiology increased significantly (10.40% vs. 8.05%, P = 0.021). The learning degree [1.00 vs. 1.05, P = 0.545] and practical application degree [1.71 vs. 1.91, P = 0.889] still remained at low level. And the potential demand degree remained at relative high level, but decreased significantly [2.50 vs. 3.33, P < 0.001]. The proportion of offering spatial epidemiology course at Chinese universities, and the proportion and degree of learning-and-application of spatial epidemiology among public health postgraduates were low, while the potential demand was high throughout the COVID-19 pandemic. To address these issues and prepare for future pandemics, integrating spatial epidemiology into the compulsory curriculum system of public health education in China would be needed.

BMC Medical Education
Guangxi Medical University (CN)
Good health and well-being
Openalex Percentile: Top 6%
COVID-19 epidemiological studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.