Medical insurance-related professionals’ identification of the benefits of the medical insurance system in Northeast China: a cross-sectional study
Abstract Background The identification of the benefits of the medical insurance system (IBMIS) serves as an important indicator of the effectiveness of medical insurance policy implementation and provides valuable feedback during the ongoing reform phase of the medical insurance system (MIS). This r...
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BMC
2025-01-01
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| Series: | BMC Health Services Research |
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| Online Access: | https://doi.org/10.1186/s12913-025-12273-z |
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| author | Zili Zhou Huan Liu Linghan Shan Ye Li Xin Zhang Jiahui Wang Zheng Kang Mingli Jiao Yanhua Hao Yangguang Chen Qunhong Wu |
| author_facet | Zili Zhou Huan Liu Linghan Shan Ye Li Xin Zhang Jiahui Wang Zheng Kang Mingli Jiao Yanhua Hao Yangguang Chen Qunhong Wu |
| author_sort | Zili Zhou |
| collection | DOAJ |
| description | Abstract Background The identification of the benefits of the medical insurance system (IBMIS) serves as an important indicator of the effectiveness of medical insurance policy implementation and provides valuable feedback during the ongoing reform phase of the medical insurance system (MIS). This research aims to analyze the IBMIS from the perspective of medical insurance-related professionals and to identify potential associate factors with IBMIS. Methods In 2021, we conducted a cross-sectional questionnaire survey (n = 1829) in Heilongjiang Province, located in Northeast China, to collect data. Interviewees were selected using a multi-stage stratified cluster random sampling method. A binary logistic regression model was employed to identify the factors influencing the IBMIS. Results Overall, 66% (1,207) of professionals associated with medical insurance exhibited a high IBMIS. The logistic regression model indicated that a high IBMIS was positively correlated with the following dimensions: policy capacity (OR = 1.562, 95% CI: 1.375–1.775), financial burden (OR = 1.512, 95% CI: 1.347–1.699), and convenience (OR = 1.300, 95% CI: 1.171–1.442). Four significant factors (P < 0.05) contributing to a high IBMIS include: “Relief of financial burden for people with serious diseases” (OR = 2.288, 95% CI: 1.740–3.007), “Medical insurance’s ability to address major problems that people expect” (OR = 1.565, 95% CI: 1.175–2.085), “Governance capacity of the medical insurance administrative agencies and management institutions” (OR = 1.472, 95% CI: 1.111–1.951), and “Effectiveness of governance for off-site medical reimbursement issues” (OR = 1.445, 95% CI: 1.036–2.014). Conclusion The policy capacity, financial burden, and convenience of the MIS have a positive impact on the IBMIS. Government departments can pay closer attention to these factors and formulate targeted intervention measures to improve the implementation effectiveness of medical insurance policies. |
| format | Article |
| id | doaj-art-38d62aa69c0a470e9ce8ae58b9e79542 |
| institution | Kabale University |
| issn | 1472-6963 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj-art-38d62aa69c0a470e9ce8ae58b9e795422025-01-26T12:22:02ZengBMCBMC Health Services Research1472-69632025-01-0125111110.1186/s12913-025-12273-zMedical insurance-related professionals’ identification of the benefits of the medical insurance system in Northeast China: a cross-sectional studyZili Zhou0Huan Liu1Linghan Shan2Ye Li3Xin Zhang4Jiahui Wang5Zheng Kang6Mingli Jiao7Yanhua Hao8Yangguang Chen9Qunhong Wu10Department of Social Medicine, School of Health Management, Harbin Medical UniversityDepartment of Social Medicine, School of Health Management, Harbin Medical UniversityDepartment of Social Medicine, School of Health Management, Harbin Medical UniversityDepartment of Social Medicine, School of Health Management, Harbin Medical UniversityDepartment of Social Medicine, School of Health Management, Harbin Medical UniversitySchool of Politics and Public Administration, Soochow UniversityDepartment of Social Medicine, School of Health Management, Harbin Medical UniversityDepartment of Social Medicine, School of Health Management, Harbin Medical UniversityDepartment of Social Medicine, School of Health Management, Harbin Medical UniversityDepartment of Health Management, School of Nursing, Guilin Medical UniversityDepartment of Social Medicine, School of Health Management, Harbin Medical UniversityAbstract Background The identification of the benefits of the medical insurance system (IBMIS) serves as an important indicator of the effectiveness of medical insurance policy implementation and provides valuable feedback during the ongoing reform phase of the medical insurance system (MIS). This research aims to analyze the IBMIS from the perspective of medical insurance-related professionals and to identify potential associate factors with IBMIS. Methods In 2021, we conducted a cross-sectional questionnaire survey (n = 1829) in Heilongjiang Province, located in Northeast China, to collect data. Interviewees were selected using a multi-stage stratified cluster random sampling method. A binary logistic regression model was employed to identify the factors influencing the IBMIS. Results Overall, 66% (1,207) of professionals associated with medical insurance exhibited a high IBMIS. The logistic regression model indicated that a high IBMIS was positively correlated with the following dimensions: policy capacity (OR = 1.562, 95% CI: 1.375–1.775), financial burden (OR = 1.512, 95% CI: 1.347–1.699), and convenience (OR = 1.300, 95% CI: 1.171–1.442). Four significant factors (P < 0.05) contributing to a high IBMIS include: “Relief of financial burden for people with serious diseases” (OR = 2.288, 95% CI: 1.740–3.007), “Medical insurance’s ability to address major problems that people expect” (OR = 1.565, 95% CI: 1.175–2.085), “Governance capacity of the medical insurance administrative agencies and management institutions” (OR = 1.472, 95% CI: 1.111–1.951), and “Effectiveness of governance for off-site medical reimbursement issues” (OR = 1.445, 95% CI: 1.036–2.014). Conclusion The policy capacity, financial burden, and convenience of the MIS have a positive impact on the IBMIS. Government departments can pay closer attention to these factors and formulate targeted intervention measures to improve the implementation effectiveness of medical insurance policies.https://doi.org/10.1186/s12913-025-12273-zMedical insurance systemMedical insurance-related professionalsIdentification of the benefitsBinary logistic regression |
| spellingShingle | Zili Zhou Huan Liu Linghan Shan Ye Li Xin Zhang Jiahui Wang Zheng Kang Mingli Jiao Yanhua Hao Yangguang Chen Qunhong Wu Medical insurance-related professionals’ identification of the benefits of the medical insurance system in Northeast China: a cross-sectional study BMC Health Services Research Medical insurance system Medical insurance-related professionals Identification of the benefits Binary logistic regression |
| title | Medical insurance-related professionals’ identification of the benefits of the medical insurance system in Northeast China: a cross-sectional study |
| title_full | Medical insurance-related professionals’ identification of the benefits of the medical insurance system in Northeast China: a cross-sectional study |
| title_fullStr | Medical insurance-related professionals’ identification of the benefits of the medical insurance system in Northeast China: a cross-sectional study |
| title_full_unstemmed | Medical insurance-related professionals’ identification of the benefits of the medical insurance system in Northeast China: a cross-sectional study |
| title_short | Medical insurance-related professionals’ identification of the benefits of the medical insurance system in Northeast China: a cross-sectional study |
| title_sort | medical insurance related professionals identification of the benefits of the medical insurance system in northeast china a cross sectional study |
| topic | Medical insurance system Medical insurance-related professionals Identification of the benefits Binary logistic regression |
| url | https://doi.org/10.1186/s12913-025-12273-z |
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