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Medical insurance and health equity in health service utilization among the middle-aged and older adults in China: a quantile regression approach
BACKGROUND: China has achieved nearly universal coverage of the Social Basic Medical Insurance (SBMI), which aims to reduce the disease burden and improve the utilization of health services. We investigated the association between China’s health insurance schemes and health service utilization of mi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302153/ https://www.ncbi.nlm.nih.gov/pubmed/32552901 http://dx.doi.org/10.1186/s12913-020-05423-y |
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author | Fan, Guorui Deng, Zhaohua Wu, Xiang Wang, Yang |
author_facet | Fan, Guorui Deng, Zhaohua Wu, Xiang Wang, Yang |
author_sort | Fan, Guorui |
collection | PubMed |
description | BACKGROUND: China has achieved nearly universal coverage of the Social Basic Medical Insurance (SBMI), which aims to reduce the disease burden and improve the utilization of health services. We investigated the association between China’s health insurance schemes and health service utilization of middle-aged and older adults at different quantiles, and then explored whether the SBMI could help reduce the underutilization of health services among the middle-aged and older adults in China. METHODS: Survey data of middle-aged and older adults were drawn from the China Health and Retirement Longitudinal Study (CHARLS). A linear quantile mixed regression model was utilized to provide a comprehensive understanding of the relationship between SBMI and health service utilization, which was measured by the total medical expenditure. We took the New Rural Cooperative Medical Scheme (NCMS) as the reference level and examined the associations of the Urban Employee Basic Medical Insurance (UEBMI) and the Urban Resident Basic Medical Insurance (URBMI) with health service utilization. RESULTS: The quantile regression analysis revealed a significant positive association between URBMI and health service utilization at the 0.75 (β = 1.608, p < 0.01), 0.8 (β = 1.578, p < 0.01), 0.85 (β = 1.473, p < 0.01), 0.9 (β = 1.403, p < 0.01) and 0.95 (β = 1.152, p < 0.01) quantiles, and also a significant positive association between UEBMI and health service utilization at the 0.85 (β = 1.196, p < 0.01), 0.9 (β = 1.070, p < 0.01) and 0.95 (β = 0.736, p < 0.01) quantiles. Results showed that URBMI was significantly associated with an improvement in inpatient health service utilization of the middle-aged and older adults, and a significant positive association between UEBMI and inpatient health service utilization was observed at 0.1 (β = 0.559, p < 0.01), 0.25 (β = 0.420, p < 0.05), 0.5 (β = 0.352, p < 0.05), and 0.75 (β = 0.306, p < 0.05) quantiles. CONCLUSIONS: Inequity in health service utilization exists among the middle-aged and older adults across urban and rural Chinese areas, and it can be explained by the different reimbursement benefits of SBMI types. |
format | Online Article Text |
id | pubmed-7302153 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73021532020-06-19 Medical insurance and health equity in health service utilization among the middle-aged and older adults in China: a quantile regression approach Fan, Guorui Deng, Zhaohua Wu, Xiang Wang, Yang BMC Health Serv Res Research Article BACKGROUND: China has achieved nearly universal coverage of the Social Basic Medical Insurance (SBMI), which aims to reduce the disease burden and improve the utilization of health services. We investigated the association between China’s health insurance schemes and health service utilization of middle-aged and older adults at different quantiles, and then explored whether the SBMI could help reduce the underutilization of health services among the middle-aged and older adults in China. METHODS: Survey data of middle-aged and older adults were drawn from the China Health and Retirement Longitudinal Study (CHARLS). A linear quantile mixed regression model was utilized to provide a comprehensive understanding of the relationship between SBMI and health service utilization, which was measured by the total medical expenditure. We took the New Rural Cooperative Medical Scheme (NCMS) as the reference level and examined the associations of the Urban Employee Basic Medical Insurance (UEBMI) and the Urban Resident Basic Medical Insurance (URBMI) with health service utilization. RESULTS: The quantile regression analysis revealed a significant positive association between URBMI and health service utilization at the 0.75 (β = 1.608, p < 0.01), 0.8 (β = 1.578, p < 0.01), 0.85 (β = 1.473, p < 0.01), 0.9 (β = 1.403, p < 0.01) and 0.95 (β = 1.152, p < 0.01) quantiles, and also a significant positive association between UEBMI and health service utilization at the 0.85 (β = 1.196, p < 0.01), 0.9 (β = 1.070, p < 0.01) and 0.95 (β = 0.736, p < 0.01) quantiles. Results showed that URBMI was significantly associated with an improvement in inpatient health service utilization of the middle-aged and older adults, and a significant positive association between UEBMI and inpatient health service utilization was observed at 0.1 (β = 0.559, p < 0.01), 0.25 (β = 0.420, p < 0.05), 0.5 (β = 0.352, p < 0.05), and 0.75 (β = 0.306, p < 0.05) quantiles. CONCLUSIONS: Inequity in health service utilization exists among the middle-aged and older adults across urban and rural Chinese areas, and it can be explained by the different reimbursement benefits of SBMI types. BioMed Central 2020-06-17 /pmc/articles/PMC7302153/ /pubmed/32552901 http://dx.doi.org/10.1186/s12913-020-05423-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Fan, Guorui Deng, Zhaohua Wu, Xiang Wang, Yang Medical insurance and health equity in health service utilization among the middle-aged and older adults in China: a quantile regression approach |
title | Medical insurance and health equity in health service utilization among the middle-aged and older adults in China: a quantile regression approach |
title_full | Medical insurance and health equity in health service utilization among the middle-aged and older adults in China: a quantile regression approach |
title_fullStr | Medical insurance and health equity in health service utilization among the middle-aged and older adults in China: a quantile regression approach |
title_full_unstemmed | Medical insurance and health equity in health service utilization among the middle-aged and older adults in China: a quantile regression approach |
title_short | Medical insurance and health equity in health service utilization among the middle-aged and older adults in China: a quantile regression approach |
title_sort | medical insurance and health equity in health service utilization among the middle-aged and older adults in china: a quantile regression approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302153/ https://www.ncbi.nlm.nih.gov/pubmed/32552901 http://dx.doi.org/10.1186/s12913-020-05423-y |
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