Cargando…

The efficiency of health resource allocation and its influencing factors: evidence from the super efficiency slack based model-Tobit model

BACKGROUND: This study aims to analyze the health resource allocation efficiency in Sichuan Province from 2010 to 2018 and provide other countries with China's experience. METHODS: We used the super efficiency slack based model (SBM) model and Malmquist index to analyze the super efficiency and...

Descripción completa

Detalles Bibliográficos
Autores principales: Gong, Jing, Shi, Leiyu, Wang, Xiaohan, Sun, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153566/
https://www.ncbi.nlm.nih.gov/pubmed/35963775
http://dx.doi.org/10.1093/inthealth/ihac054
_version_ 1785035954259492864
author Gong, Jing
Shi, Leiyu
Wang, Xiaohan
Sun, Gang
author_facet Gong, Jing
Shi, Leiyu
Wang, Xiaohan
Sun, Gang
author_sort Gong, Jing
collection PubMed
description BACKGROUND: This study aims to analyze the health resource allocation efficiency in Sichuan Province from 2010 to 2018 and provide other countries with China's experience. METHODS: We used the super efficiency slack based model (SBM) model and Malmquist index to analyze the super efficiency and inter-period efficiency of health resource allocation in 19 cities in Sichuan Province from 2010 to 2018 and propose the input-output optimization scheme of health resource allocation in 2018. Finally, the Tobit model was used to estimate the influencing factors of health resource allocation efficiency. RESULTS: The total allocation of health resources in Sichuan Province was increasing in addition to the total number of visits from 2010 to 2018. The super efficiency SBM results identified that the sample's average score was between 0.651 and 3.244, with an average of 1.041, of which 15 cities had not reached data envelopment analysis effectiveness. According to the Malmquist index, the average total factor productivity index of Sichuan Province was 0.930, which showed an imbalance in resource input, and its fluctuation was mainly related to the technological progress index and scale efficiency. The efficiency score was affected by the average annual income of residents, population density and education level. CONCLUSIONS: The amount of health resource allocation in Sichuan Province had shown an overall upward trend since 2010. However, resource allocation efficiency was not high, and there were problems such as significant regional differences, insufficient technological innovation capabilities and unscientific allocation of resource scale. To optimize the resource allocation structure, we suggest that the relevant departments pay attention to the impact of natural disasters, the average annual income of residents, population density and education level on efficiency to allocate health resources scientifically.
format Online
Article
Text
id pubmed-10153566
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-101535662023-05-03 The efficiency of health resource allocation and its influencing factors: evidence from the super efficiency slack based model-Tobit model Gong, Jing Shi, Leiyu Wang, Xiaohan Sun, Gang Int Health Original Article BACKGROUND: This study aims to analyze the health resource allocation efficiency in Sichuan Province from 2010 to 2018 and provide other countries with China's experience. METHODS: We used the super efficiency slack based model (SBM) model and Malmquist index to analyze the super efficiency and inter-period efficiency of health resource allocation in 19 cities in Sichuan Province from 2010 to 2018 and propose the input-output optimization scheme of health resource allocation in 2018. Finally, the Tobit model was used to estimate the influencing factors of health resource allocation efficiency. RESULTS: The total allocation of health resources in Sichuan Province was increasing in addition to the total number of visits from 2010 to 2018. The super efficiency SBM results identified that the sample's average score was between 0.651 and 3.244, with an average of 1.041, of which 15 cities had not reached data envelopment analysis effectiveness. According to the Malmquist index, the average total factor productivity index of Sichuan Province was 0.930, which showed an imbalance in resource input, and its fluctuation was mainly related to the technological progress index and scale efficiency. The efficiency score was affected by the average annual income of residents, population density and education level. CONCLUSIONS: The amount of health resource allocation in Sichuan Province had shown an overall upward trend since 2010. However, resource allocation efficiency was not high, and there were problems such as significant regional differences, insufficient technological innovation capabilities and unscientific allocation of resource scale. To optimize the resource allocation structure, we suggest that the relevant departments pay attention to the impact of natural disasters, the average annual income of residents, population density and education level on efficiency to allocate health resources scientifically. Oxford University Press 2022-08-13 /pmc/articles/PMC10153566/ /pubmed/35963775 http://dx.doi.org/10.1093/inthealth/ihac054 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Gong, Jing
Shi, Leiyu
Wang, Xiaohan
Sun, Gang
The efficiency of health resource allocation and its influencing factors: evidence from the super efficiency slack based model-Tobit model
title The efficiency of health resource allocation and its influencing factors: evidence from the super efficiency slack based model-Tobit model
title_full The efficiency of health resource allocation and its influencing factors: evidence from the super efficiency slack based model-Tobit model
title_fullStr The efficiency of health resource allocation and its influencing factors: evidence from the super efficiency slack based model-Tobit model
title_full_unstemmed The efficiency of health resource allocation and its influencing factors: evidence from the super efficiency slack based model-Tobit model
title_short The efficiency of health resource allocation and its influencing factors: evidence from the super efficiency slack based model-Tobit model
title_sort efficiency of health resource allocation and its influencing factors: evidence from the super efficiency slack based model-tobit model
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153566/
https://www.ncbi.nlm.nih.gov/pubmed/35963775
http://dx.doi.org/10.1093/inthealth/ihac054
work_keys_str_mv AT gongjing theefficiencyofhealthresourceallocationanditsinfluencingfactorsevidencefromthesuperefficiencyslackbasedmodeltobitmodel
AT shileiyu theefficiencyofhealthresourceallocationanditsinfluencingfactorsevidencefromthesuperefficiencyslackbasedmodeltobitmodel
AT wangxiaohan theefficiencyofhealthresourceallocationanditsinfluencingfactorsevidencefromthesuperefficiencyslackbasedmodeltobitmodel
AT sungang theefficiencyofhealthresourceallocationanditsinfluencingfactorsevidencefromthesuperefficiencyslackbasedmodeltobitmodel
AT gongjing efficiencyofhealthresourceallocationanditsinfluencingfactorsevidencefromthesuperefficiencyslackbasedmodeltobitmodel
AT shileiyu efficiencyofhealthresourceallocationanditsinfluencingfactorsevidencefromthesuperefficiencyslackbasedmodeltobitmodel
AT wangxiaohan efficiencyofhealthresourceallocationanditsinfluencingfactorsevidencefromthesuperefficiencyslackbasedmodeltobitmodel
AT sungang efficiencyofhealthresourceallocationanditsinfluencingfactorsevidencefromthesuperefficiencyslackbasedmodeltobitmodel