ArticleHealth science reports2023
Research on the socioeconomic factors that influence the development of voluntary, non-remunerated blood donation in China-A correlation and regression analysis based on data from 2012 to 2018.
Article in Health science reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Factors associated with blood donation among college and university students in Wuhan, China: structural equation model.BMC public health · 2024Article
- Research on the socioeconomic factors that influence the development of voluntary, non-remunerated blood donation in China-A correlation and regression analysis based on data from 2012 to 2018.Health science reports · 2023Article
- Blood Donation Knowledge, Attitudes, and Practices Amid Instability: A Biphasic Cross-Sectional Study in West Bank, Palestine (2022 vs 2025).SAGE open nursingArticle
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Authors and funding
4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Previous research has shown that socioeconomic factors, such as income and education, are associated with blood donation behavior. This study aims to investigate the factors that influence blood donation behavior using a large database of blood donation records and to provide further empirical evidence and insights into the factors that influence blood donation behavior and to identify potential strategies to increase blood donation rates in China. Methods: This study employed correlation analysis and regression analysis to investigate the impact of socioeconomic factors on blood donation rates in 36 major cities in China. The study also used the K-Means clustering algorithm to identify the socioeconomic factors that influence blood donation behavior at the per capita level. Additionally, the study conducted correlation analysis between the donor's age groups and the socioeconomic factors. The indices of economy and education were determined by correlation analysis. Results: At the city level, overall economic output and university student populations were positively correlated with total blood donation times. However, at the per capita level, this linear relationship disappeared. By applying the K-Means clustering algorithm, the cities were divided into subgroups where the linear relationship reemerged. The study also found that the number of college students had a linear relationship with blood donors between the ages of 18-34. Conclusion: City characteristics and demographics significantly impact blood donation rates in different ways. A universal policy may have limited effect given the heterogeneity across cities. Tailored interventions targeting specific city clusters and education levels are needed to promote blood donation behavior, especially among youth and students. Investment in education and targeted education on blood donation appear particularly impactful. A combined approach integrating social and economic policies will likely be most effective.
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