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Interpretation of China’s 2017 health expenditure: a latent profile analysis of panel data
OBJECTIVE: To explore the latent structure of health financing and the institutional distribution of health expenditure (focused on hospital expenditure) in provinces, autonomous regions and municipalities of mainland China, and to examine how these profiles may be related to their externalising and...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328737/ https://www.ncbi.nlm.nih.gov/pubmed/32606058 http://dx.doi.org/10.1136/bmjopen-2019-035512 |
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author | Zang, Shuang Zhao, Meizhen OuYang, Jing Wang, Xin |
author_facet | Zang, Shuang Zhao, Meizhen OuYang, Jing Wang, Xin |
author_sort | Zang, Shuang |
collection | PubMed |
description | OBJECTIVE: To explore the latent structure of health financing and the institutional distribution of health expenditure (focused on hospital expenditure) in provinces, autonomous regions and municipalities of mainland China, and to examine how these profiles may be related to their externalising and internalising characteristics. STUDY DESIGN: The study used panel data harvested from the China National Health Accounts Report 2018. METHODS: Mainland China’s provincial data on health expenditure in 2017 was studied. A latent profile analysis was conducted to identify health financing and hospital health expenditure profiles in China. Additionally, rank-sum tests were used to understand the difference of socioeconomic indicators between subgroups. RESULTS: A best-fitting three-profile solution for per capita health financing was identified, with government health expenditure (χ(2)=10.137, p=0.006) and social health expenditure (χ(2)=6.899, p=0.032) varying significantly by profiles. Health expenditure in hospitals was subject to a two-profile solution with health expenditure flow to urban hospitals, county hospitals and community health service centres having significant differences between the two profiles (p<0.001). CONCLUSIONS: Per capita health financing and health expenditure spent in hospitals have discrepant socioeconomic characteristics in different profiles, which may be attributed to macroeconomic factors and government policies. The study provided new and explicit ideas for health financing and health policy regulation in China. |
format | Online Article Text |
id | pubmed-7328737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-73287372020-07-02 Interpretation of China’s 2017 health expenditure: a latent profile analysis of panel data Zang, Shuang Zhao, Meizhen OuYang, Jing Wang, Xin BMJ Open Health Economics OBJECTIVE: To explore the latent structure of health financing and the institutional distribution of health expenditure (focused on hospital expenditure) in provinces, autonomous regions and municipalities of mainland China, and to examine how these profiles may be related to their externalising and internalising characteristics. STUDY DESIGN: The study used panel data harvested from the China National Health Accounts Report 2018. METHODS: Mainland China’s provincial data on health expenditure in 2017 was studied. A latent profile analysis was conducted to identify health financing and hospital health expenditure profiles in China. Additionally, rank-sum tests were used to understand the difference of socioeconomic indicators between subgroups. RESULTS: A best-fitting three-profile solution for per capita health financing was identified, with government health expenditure (χ(2)=10.137, p=0.006) and social health expenditure (χ(2)=6.899, p=0.032) varying significantly by profiles. Health expenditure in hospitals was subject to a two-profile solution with health expenditure flow to urban hospitals, county hospitals and community health service centres having significant differences between the two profiles (p<0.001). CONCLUSIONS: Per capita health financing and health expenditure spent in hospitals have discrepant socioeconomic characteristics in different profiles, which may be attributed to macroeconomic factors and government policies. The study provided new and explicit ideas for health financing and health policy regulation in China. BMJ Publishing Group 2020-06-30 /pmc/articles/PMC7328737/ /pubmed/32606058 http://dx.doi.org/10.1136/bmjopen-2019-035512 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Health Economics Zang, Shuang Zhao, Meizhen OuYang, Jing Wang, Xin Interpretation of China’s 2017 health expenditure: a latent profile analysis of panel data |
title | Interpretation of China’s 2017 health expenditure: a latent profile analysis of panel data |
title_full | Interpretation of China’s 2017 health expenditure: a latent profile analysis of panel data |
title_fullStr | Interpretation of China’s 2017 health expenditure: a latent profile analysis of panel data |
title_full_unstemmed | Interpretation of China’s 2017 health expenditure: a latent profile analysis of panel data |
title_short | Interpretation of China’s 2017 health expenditure: a latent profile analysis of panel data |
title_sort | interpretation of china’s 2017 health expenditure: a latent profile analysis of panel data |
topic | Health Economics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328737/ https://www.ncbi.nlm.nih.gov/pubmed/32606058 http://dx.doi.org/10.1136/bmjopen-2019-035512 |
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