Analysis of influencing factors and equity in health education for mobile populations based on random forest model

Evidence from the China migrants dynamic survey

Bibliographic Data

ID22066838
AuthorsMeng Jiang (0000-0001-9854-3676, Zhejiang Chinese Medical University), Meng Han Jiang, Shan Yan Lei (First Affiliated Hospital Zhejiang University), Fang Yang (0000-0003-0696-6650, Zhejiang Chinese Medical University, corresponding author)
Year2025
Volume13
Pages1570633-1570633
Publication date2025-05-08
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1570633
PMID40406091
OpenAlexW4410212525
LanguageEN
Citations received2
References cited44

Background: Strengthening health education is an important measure to improve the health of mobile populations and a key objective of China's basic public health services. Existing studies demonstrate that health education affects the health of mobile populations, but insufficient attention is paid to the importance of factors that influence health education. Moreover, few studies examine how these factors contribute to health education equity among mobile populations in China. Therefore, this study aims to reveal the importance of factors affecting health education based on a comprehensive understanding of mobile populations' overall health education status. Furthermore, the contribution of these important factors to health education equity is analyzed to inform differentiated intervention strategies, thereby providing a reference for enhancing mobile populations' health level and achieving equal access to basic public health services. Methods: This study utilized data from the 2018 China Migrants Dynamic Survey (CMDS), with a final sample of 103,910 participants after data cleaning. Chi-square tests were first conducted to examine differences in health education across various characteristics of the mobile population. The relative importance of influencing factors was then assessed using a random forest model, followed by key factor identification through LASSO regression. Subsequently, binary logistic regression was performed to quantify the effects of these key factors. Finally, concentration indices were calculated to identify these factors' contributions to health education equity. Results: The self-assessed health status of China's mobile population was good, with 81.89% reporting receipt of health education, while 18.11% had not received any health education. Seven key factors were identified as most influential in determining health education access among the mobile population: income, education, age, health record, scope of mobility, reason for mobility and gender. The health education concentration index of the mobile population was 0.0121, indicating a significant inequality in the utilization of health education services. Each important factor had a significant negative amplifying effect on health education equity among the mobile population. Conclusion: Health education among the mobile population requires further enhancement. Special attention should be directed toward vulnerable groups, including low-income individuals, the older adult, those with lower educational levels, and those with wider migration ranges. Moreover, the impact of key factors on health education equity among the mobile population should be carefully considered to improve health education equity

Business · China · Economic growth · Economics · Environmental health · Geography · Health care · Health equity · Political science · Random forest · Computer Science · Global Maternal and Child Health · Healthcare Systems and Reforms · Medicine · Migration, Health and Trauma

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