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Pu W, Yue X, Xiong Q, Jia K, Zou Y. The analysis of GM (1, 1) grey model to predict the health resource allocation in Jilin Province, China: An observational study. Medicine (Baltimore) 2024; 103:e39298. [PMID: 39183402 PMCID: PMC11346830 DOI: 10.1097/md.0000000000039298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 04/16/2024] [Accepted: 07/23/2024] [Indexed: 08/27/2024] Open
Abstract
To predict the development of health resource allocation in Jilin Province during the 14th 5-Year Plan period, and to provide a scientific basis for promoting the improvement of its service capacity. The data of the health resource from 2015 to 2022 were obtained from the Jilin Statistical Yearbook, and the number of medical institutions, medical beds, health technicians, licensed (assistant) physicians, registered nurses and pharmacists were selected as evaluation indicators, and the grey prediction model constructed by Python was used to predict the development from 2023 to 2025. In the 14th 5-Year Plan period, the health resource in Jilin Province showed an increasing trend, and it is predicted that in 2025, the number of medical institutions, medical beds, health technicians, licensed (assistant) physicians, registered nurses, and pharmacists in Jilin Province will reach 28,999, 196,328, 262,219, 101,273, 129,586, and 9469, respectively. Except that the pharmacist team failed to meet the planning objectives of the 14th 5-Year Plan, the remaining health resources could meet the planning requirements. The allocation level of health resources in Jilin Province has been continuously improved, but it still faces the problems that the allocation of medical beds needs to be optimized, the doctor-nurse ratio needs to be improved, the reserve of registered nurses is insufficient, there is a gap in the pharmacist team, and the development of pharmacy services is slow.
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Affiliation(s)
- Wanxu Pu
- Changchun University of Chinese Medicine, Changchun, Jilin Province, China
| | - Xitao Yue
- Changchun University of Chinese Medicine, Changchun, Jilin Province, China
| | - Qi Xiong
- Changchun University of Chinese Medicine, Changchun, Jilin Province, China
| | - Kaikai Jia
- Changchun University of Chinese Medicine, Changchun, Jilin Province, China
| | - Yuanjun Zou
- Changchun University of Chinese Medicine, Changchun, Jilin Province, China
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Zhou M. The allocation and utilization efficiency of hospital beds in Sichuan Province, China. Medicine (Baltimore) 2024; 103:e39329. [PMID: 39151534 PMCID: PMC11332740 DOI: 10.1097/md.0000000000039329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 08/19/2024] Open
Abstract
OBJECTIVE To analyze the allocation and utilization efficiency of hospital beds in Sichuan Province, China, and to provide a scientific basis for improving the rational allocation and efficient utilization. METHODS The supply and demand balance method, health resource agglomeration degree (HRAD), bed efficiency index and bed utilization model were used to evaluate the allocation and utilization efficiency of hospital beds in Sichuan Province from 2017 to 2021. RESULTS The number of hospital beds per 1000 population in Sichuan Province increased from 4.97 in 2017 to 5.94 in 2021. The overall supply and demand ratio of hospital beds in Sichuan Province is between 0.85 and 1.01, and the supply and demand situation is a basically balanced situation. The HRAD of hospital beds in Ya'an, Aba, Ganzi and Liangshan is <1, indicating that the equity of hospital beds by geography in these regions is low. The difference between HRAD and population agglomeration degree (PAD) in 9 regions, including Deyang, Aba, Ganzi and Liangshan, is <0, indicating that there are insufficient hospital beds in these areas relative to the agglomerated population. The bed efficiency index of hospital beds in 17 regions, including Chengdu, Zigong, Aba and Ganzi, are all <1, which means that hospital beds are operating with low efficiency. The bed utilization model of Panzhihua is efficiency type, that of Zigong, Deyang and Ziyang is pressure bed type, and that of Nanchong and Ya'an is idle type. CONCLUSION The hospital bed allocation in Sichuan Province is relatively good, and the supply and demand situation is in a basically balanced situation. The hospital bed allocation in Aba, Ganzi and Liangshan is insufficient by geography and population. The overall operational efficiency of hospital beds is low, and there are more idle and pressure bed utilization models.
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Affiliation(s)
- Minghua Zhou
- Department of Administration Office, Luzhou People’s Hospital, Luzhou, Sichuan, China
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Zhu L, Gao W, Zhang S, Yu F, Li J, Feng J, Wang R. Equality of healthcare resource allocation between impoverished counties and non-impoverished counties in Northwest China: a longitudinal study. BMC Health Serv Res 2024; 24:830. [PMID: 39039513 PMCID: PMC11265348 DOI: 10.1186/s12913-024-11312-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 07/12/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND The Health and Medical Assistance Program for Poverty Alleviation is part of China's targeted poverty elimination strategy, which aims to protect poor people's right to health and prevent them from becoming trapped in or returning to poverty because of illness. Many tasks have been defined in this program, including raising the medical insurance level, providing a triage system, improving medical and health services, and enhancing people's health. One pivotal aspect of this initiative involves equitable health resource allocation, a key measure aimed at bolstering medical and health services. This study aimed to analyze and compare health resource allocations in different counties in Northwest China after the implementation of the program. METHODS The Gini coefficient quantifies the level of distributional equality, the Theil index assesses the sources of inequality, and the Health Resource Agglomeration Degree gauges the accessibility of health resources. RESULTS 1) The health resource allocation distributed based on population(Gini Coefficient < 0.45) was more equitable than that distributed based on area(Gini Coefficient > 0.35) among counties in Northwest China. 2) The contribution rate within non-impoverished counties is higher than that of impoverished counties, which means the inequality within non-impoverished counties. 3) The allocation of beds in medical institutions by area in non-impoverished counties was better than that in impoverished counties, and accessibility to health services for residents in non-impoverished counties was better than that in impoverished counties. CONCLUSION The analysis of health resource allocation among the five provinces in Northwest China revealed significant differences in equality among the five provinces in Northwest China, and the differences were mainly derived from the non-impoverished counties. Although the equality is gradually improving, the number of health resources in impoverished counties remain lower than that in non-impoverished counties.Subsequently, it is essential to ensure equitable distribution of healthcare resources while also taking into account their utilization and quality.
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Affiliation(s)
- Liang Zhu
- Department of Health Service Management and Medical Education, School of Public Health, Air Force Medical University, No. 169, Changle West Road, Xincheng District, Xi'an, Shaanxi Province, 710032, China
- The Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, Shaanxi, 710032, China
| | - Wei Gao
- Teaching Archives Room, Air Force Medical University, Xi'an, Shaanxi, 710032, China
| | - Siyu Zhang
- Department of Health Service Management and Medical Education, School of Public Health, Air Force Medical University, No. 169, Changle West Road, Xincheng District, Xi'an, Shaanxi Province, 710032, China.
- The Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, Shaanxi, 710032, China.
| | - Fei Yu
- Teaching and Evaluating Center, Air Force Medical University, Xi'an, Shaanxi, 710032, China
| | - Jiaxue Li
- Department of Health Service Management and Medical Education, School of Public Health, Air Force Medical University, No. 169, Changle West Road, Xincheng District, Xi'an, Shaanxi Province, 710032, China
- The Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, Shaanxi, 710032, China
| | - Junqiang Feng
- Xi'an International Medical Center Hospital, Xi'an, Shaanxi Province, 710000, China
| | - Rui Wang
- Library of Teaching and Research Support Center, Air Force Medical University, Xi'an, Shaanxi, 710032, China
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Zhang J. Dynamic evolution of utilization efficiency of medical and health services in China. PLoS One 2024; 19:e0304157. [PMID: 38917186 PMCID: PMC11198896 DOI: 10.1371/journal.pone.0304157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 05/06/2024] [Indexed: 06/27/2024] Open
Abstract
In order to optimize the Chinese medical and health system and improve people's health level, the SFA Malmquist model, the spatial econometric model, and the standard deviation ellipse method were used to measure the efficiency of medical and health services in China's 31 provinces between 2010 and 2020. Study results indicated that the average efficiency value of the 31 provinces generally exceeded 0.8. Specifically, the average efficiency values in the eastern and central regions increased from 0.852 to 0.875 and from 0.858 to 0.88, respectively. In the western and northeastern regions, these values rose from 0.804 to 0.835 and from 0.827 to 0.854, respectively. From the perspective of spatial distribution, there were high-high and low-low clusters in most provinces with significant spatial dependence among them. This analysis reveals that medical and health services efficiency in China demonstrates a spatial pattern extending from northeast to southwest.
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Affiliation(s)
- Jing Zhang
- Medical Economic and Management School of Anhui University of Chinese Medicine, Hefei, China
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Tan H. Equity and Prediction of Bed Allocation of the Department of Stomatology in Chinese Hospitals. Risk Manag Healthc Policy 2024; 17:1465-1476. [PMID: 38855045 PMCID: PMC11162239 DOI: 10.2147/rmhp.s461085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 05/25/2024] [Indexed: 06/11/2024] Open
Abstract
Objective To analyze the equity of bed allocation of the department of stomatology in Chinese hospitals and predict the development in the next 5 years, so as to provide a scientific basis for promoting the development of oral health. Methods Data on the beds of the department of stomatology in Chinese hospitals from 2017 to 2021 were obtained from the China Health Statistical Yearbook. The Gini coefficient, Lorenz curve, Theil index and agglomeration degree were used to analyze the equity of the bed allocation, and the grey prediction model GM(1,1) was used to predict the development from 2022 to 2026. Results From 2017 to 2020, the Gini coefficient of bed allocation of the department of stomatology in Chinese hospitals was below 0.2 by population. From 2017 to 2021, the Gini coefficient of beds was above 0.6 by geography and between 0.2 and 0.3 by economy. The Theil index of beds ranged from 0.022 to 0.056 by population, from 0.532 to 0.564 by geography, and from 0.042 to 0.047 by economy. The inequity in the allocation by population was mainly from between regions, and the inequity in the allocation by geography and economy was mainly from within regions. Health resource agglomeration degree (HRAD) was greater than 2 in the eastern and central regions and less than 1 in the western region. HRAD/ population agglomeration degree (PAD) was greater than 1 in the northeast, eastern, and central regions and less than 1 in the western region. According to the prediction, the number of beds of the department of stomatology in Chinese hospitals will continue to increase, reaching 47,862.485 in 2026. Conclusion The equity of bed allocation was better by population and economy than by geography. The equity of beds in the western region is insufficient equity by population and geography, and the equity of beds in the eastern region is insufficient equity by economy.
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Affiliation(s)
- Hong Tan
- Department of Periodontal Mucosal Disease, The Affiliated Stomatology Hospital of Southwest Medical University, Oral & Maxillofacial Reconstruction and Regeneration of Luzhou Key Laboratory, Luzhou, Sichuan, 646000, People’s Republic of China
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Yu Y, Lu J, Dou X, Yi Y, Zhou L. Spatial distribution and influencing factors of CDC health resources in China: a study based on panel data from 2016-2021. Front Public Health 2024; 12:1331522. [PMID: 38751586 PMCID: PMC11094321 DOI: 10.3389/fpubh.2024.1331522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 04/19/2024] [Indexed: 05/18/2024] Open
Abstract
Background Measuring the development of Chinese centers for disease control and prevention only by analyzing human resources for health seems incomplete. Moreover, previous studies have focused more on the quantitative changes in healthcare resources and ignored its determinants. Therefore, this study aimed to analyze the allocation of healthcare resources in Chinese centers for disease control and prevention from the perspective of population and spatial distribution, and to further explore the characteristics and influencing factors of the spatial distribution of healthcare resources. Methods Disease control personnel density, disease control and prevention centers density, and health expenditures density were used to represent human, physical, and financial resources for health, respectively. First, health resources were analyzed descriptively. Then, spatial autocorrelation was used to analyze the spatial distribution characteristics of healthcare resources. Finally, we used spatial econometric modeling to explore the influencing factors of healthcare resources. Results The global Moran index for disease control and prevention centers density decreased from 1.3164 to 0.2662 (p < 0.01), while the global Moran index for disease control personnel density increased from 0.4782 to 0.5067 (p < 0.01), while the global Moran index for health expenditures density was statistically significant only in 2016 (p < 0.1). All three types of healthcare resources showed spatial aggregation. Population density and urbanization have a negative impact on the disease control and prevention centers density. There are direct and indirect effects of disease control personnel density and health expenditures density. Population density and urbanization had significant negative effects on local disease control personnel density. Urbanization has an indirect effect on health expenditures density. Conclusion There were obvious differences in the spatial distribution of healthcare resources in Chinese centers for disease control and prevention. Social, economic and policy factors can affect healthcare resources. The government should consider the rational allocation of healthcare resources at the macro level.
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Affiliation(s)
| | | | | | | | - Ling Zhou
- School of Public Health, Dalian Medical University, Dalian, China
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Luo F, Huang Y, Jiang L, Fan Q, Gou Z. Ethnic disparities and temporal trends in health resource allocation: a retrospective decadal analysis in Sichuan, a multi-ethnic Province of Southwest China (2009-2019). BMC Health Serv Res 2024; 24:541. [PMID: 38678273 PMCID: PMC11056051 DOI: 10.1186/s12913-024-11036-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 04/23/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND Research on health resource allocation trends in ethnic minority and impoverished areas in China is limited since the 2009 Medical Reform. This study aimed to investigate the variations and inequalities in health resource distribution among ethnic minority, poverty-stricken, and non-minority regions in Sichuan Province, a multi-ethnic province in Southwest China, from 2009 to 2019. METHODS The numbers of beds, doctors and nurses were retrospectively sourced from the Sichuan Health Statistics Yearbook between 2009 and 2019. All the 181 counties in Sichuan Province were categorized into five groups: Yi, Zang, other ethnic minority, poverty-stricken, and non-minority county. The Theil index, adjusted for population size, was used to evaluate health resource allocation inequalities. RESULTS From 2009 to 2019, the number of beds (Bedp1000), doctors (Docp1000), and nurses (Nurp1000) per 1000 individuals in ethnic minority and poverty-stricken counties consistently remained lower than non-minority counties. The growth rates of Bedp1000 in Yi (140%) and other ethnic minority counties (127%) were higher than in non-minority counties (121%), while the growth rates of Docp1000 in Yi (20%) and Zang (11%) counties were lower than non-minority counties (61%). Docp1000 in 33% and 50% of Yi and Zang ethnic counties decreased, respectively. Nurp1000 in Yi (240%) and other ethnic minority (316%) counties increased faster than non-minority counties (198%). The Theil index for beds and nurses declined, while the index for doctors increased. Key factors driving increases in bed allocation include preferential policies and economic development levels, while health practitioner income, economic development levels and geographical environment significantly influence doctor and nurse allocation. CONCLUSIONS Preferential policies have been successful in increasing the number of beds in health facilities, but not healthcare workers, in ethnic minority regions. The ethnic disparities in doctor allocation increased in Sichuan Province. To increase the number of doctors and nurses in ethnic minority and poverty-stricken regions, particularly in Yi counties, more preferential policies and resources should be introduced.
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Affiliation(s)
- Fang Luo
- Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, China
| | - Yuezhou Huang
- Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, China
| | - Linshan Jiang
- Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, China
| | - Qingqing Fan
- Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, China
| | - Zongchao Gou
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China.
- Breast Disease Center, West China Hospital, Sichuan University, Chengdu, China.
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Xiong C, Xia Y, Chen H, Cheng J. Regional Inequality and Associated Factors of Emergency Medicine Beds Distribution in China. Int J Public Health 2024; 69:1606812. [PMID: 38651036 PMCID: PMC11033296 DOI: 10.3389/ijph.2024.1606812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 03/06/2024] [Indexed: 04/25/2024] Open
Abstract
Objective: The regional inequality of emergency medicine beds distribution has a great impact on population health as well as the accessibility of emergency services. This study aimed to explore the regional inequality of emergency medicine bed distribution and its influencing factors. Methods: The Gini coefficient and health resource agglomeration were used to analyze the regional inequality of emergency medicine beds distribution by area from 2012 to 2021 in China. Grey correlation models were used to explore the factors influencing the regional inequality of emergency medicine beds distribution. Results: From 2012 to 2021, Gini coefficients of emergency medicine beds distribution by geographic in China showed a worsening trend, rising from 0.6229 to 0.6636. The average HRAD index was 3.43 in the east and 0.44 in the west. Population structure factors have the greatest influence on the regional inequality of emergency medicine beds distribution. Conclusion: Health resources allocation strategy only according to population size should be changed. In formulating policies for emergency medicine beds allocation should take into account population structure, financial structure of expenditure, the inequality of geographical distribution and so on.
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Affiliation(s)
- Change Xiong
- School of Public Health, Wuhan University of Science and Technology, Wuhan, Hubei, China
| | - Ying Xia
- Institute of Nursing Research, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, Hubei, China
| | - Huihui Chen
- School of Public Health, Wuhan University of Science and Technology, Wuhan, Hubei, China
| | - Jing Cheng
- School of Public Health, Wuhan University of Science and Technology, Wuhan, Hubei, China
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Qian Y, Wang X, Huang X, Li J, Jin C, Chen J, Sha M. Bounded rationality in healthcare: unraveling the psychological factors behind patient satisfaction in China. Front Psychol 2024; 15:1296032. [PMID: 38605837 PMCID: PMC11008602 DOI: 10.3389/fpsyg.2024.1296032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 03/19/2024] [Indexed: 04/13/2024] Open
Abstract
Introduction Patient satisfaction is a crucial metric to gauge the quality of medical services, but the psychological factors influencing patient satisfaction remain insufficiently explored. Methods This study examines these psychological factors by applying the theory of bounded rationality to 1,442 inpatients in Hangzhou, China, whose data were collected using a questionnaire. One-way ANOVA, correlation analysis, and hierarchical regression were used to analyze patient satisfaction and its associated factors. Additionally, the path analysis of the structural equation model revealed the mechanisms behind the key psychological factors that influenced patient satisfaction. Results Medical risk perception, the social cognition of the medical environment, and social desirability bias had significant positive impacts on patient satisfaction. By contrast, negative emotions had a significant negative impact on patient satisfaction. Notably, patients' negative emotions had both a suppressive effect and a positive moderating effect on the relationship between medical risk perception and patient satisfaction. Similarly, social desirability bias had a suppressive effect on the correlation between the social cognition of the medical environment and patient satisfaction, albeit with a negative moderating effect. Discussion These results suggest that when evaluating and improving patient satisfaction, accounting only for the factors that directly influence medical service quality is insufficient, as the indirect and moderating effects of patients' negative emotions and the social cognition of the medical environment must also be considered. Medical service providers should thus address patients' negative emotions, establish good doctor-patient relationships, optimize service environments, provide managers with medical risk education and training on negative emotions, and prioritize patient-centered care. Additionally, the government and relevant health departments should optimize medical policies, enhance fairness and accessibility, and create a positive social cognitive environment through public education and awareness campaigns.
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Affiliation(s)
- Yu Qian
- Department of Health Policy and Management, School of Public Health, Hangzhou Normal University, Hangzhou, China
- Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University, Hangzhou, China
| | - Xiaohe Wang
- Department of Health Policy and Management, School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Xianhong Huang
- Department of Health Policy and Management, School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Jinwen Li
- Department of Health Policy and Management, School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Chen Jin
- Department of Health Policy and Management, School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Jie Chen
- The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - MengYi Sha
- Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University, Hangzhou, China
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Zhao X, Wu S, Yan B, Liu B. New evidence on the real role of digital economy in influencing public health efficiency. Sci Rep 2024; 14:7190. [PMID: 38531934 DOI: 10.1038/s41598-024-57788-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 03/21/2024] [Indexed: 03/28/2024] Open
Abstract
In recent years, the rapid advancement of digital technology has supported the growth of the digital economy. The transformation towards digitization in the public health sector serves as a key indicator of this economic shift. Understanding how the digital economy continuously improves the efficiency of public health services and its various pathways of influence has become increasingly important. It is essential to clarify the impact mechanism of the digital economy on public health services to optimize health expenditures and advance digital economic construction. This study investigates the impact of digital economic development on the efficiency of public health services from a novel perspective, considering social media usage and urban-rural healthcare disparities while constructing a comprehensive index of digital economic development. The findings indicate that the digital economy reduces the efficiency of public health services primarily through two transmission mechanisms: the promotion of social media usage and the widening urban-rural healthcare gap. Moreover, these impacts and transmission pathways exhibit spatial heterogeneity. This study unveils the intrinsic connection and mechanisms of interaction between digital economic development and the efficiency of public health services, providing a theoretical basis and reference for government policy formulation. However, it also prompts further considerations on achieving synergy and interaction between the digital economy and public health services.
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Affiliation(s)
- Xiongfei Zhao
- School of Economics and Management, Beijing University of Technology, Beijing, 100124, China
| | - Shansong Wu
- School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, 116025, China.
| | - Bin Yan
- School of Management Engineering & E-Commerce, Zhejiang Gongshang University, Hangzhou, 310018, China
| | - Baoliu Liu
- School of Economics and Management, Beijing University of Technology, Beijing, 100124, China
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11
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Zhou M. Regional differences in health resource allocation: a longitudinal study in the Chengdu-Chongqing economic circle, China. BMJ Open 2024; 14:e082721. [PMID: 38514148 PMCID: PMC10961522 DOI: 10.1136/bmjopen-2023-082721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 03/11/2024] [Indexed: 03/23/2024] Open
Abstract
OBJECTIVE To analyse regional differences in health resource allocation in the Chengdu-Chongqing economic circle. DESIGN A longitudinal analysis that collected data on health resource allocation from 2017 to 2021. SETTING The number of beds, health technicians, licensed (assistant) physicians, registered nurses and financial allocations per 1000 population in the 42 regions of Chengdu-Chongqing economic circle were used for the analysis. METHODS The entropy weight technique for order preference by similarity to an ideal solution (TOPSIS) method and the rank sum ratio (RSR) method were used to evaluate the health resource allocation. RESULTS The number of licensed (assistant) physicians per 1000 population in the Chengdu-Chongqing economic circle (3.01) was lower than the average in China (3.04) in 2021. According to the entropy weight-TOPSIS method, Yuzhong in Chongqing had the largest C-value and the highest ranking. Jiangbei in Chongqing and Chengdu and Ya'an in Sichuan Province had higher C-values and were ranked in the top 10. Jiangjin, Hechuan, Tongnan and Zhongxian in Chongqing and Guang'an in Sichuan Province had lower C-values and were all ranked after the 30th place. According to the RSR method, the 42 regions were divided into three grades of good, medium and poor. The health resource allocations of Yuzhong, Jiangbei, Nanchuan, Jiulongpo and Shapingba in Chongqing and Chengdu and Ya'an in Sichuan Province were of good grade, those of Tongnan, Jiangjin, Yubei and Dazu in Chongqing and Guang'an and Dazhou in Sichuan Province were of poor grade, and the rest of the regions were of medium grade. CONCLUSION The regional differences in health resource allocation in the Chengdu-Chongqing economic circle were more obvious, the health resource allocation in Chongqing was more polarised and the health resource allocation in Sichuan Province was more balanced, but the advantaged regions were not prominent enough.
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Affiliation(s)
- Minghua Zhou
- Administration Office, Luzhou People's Hospital, Luzhou, Sichuan, China
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12
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Wei Z, Wei K, Li Y, Nie L, Zhou Y. Measurement of China's public health level: compilation and research of an index. BMC Public Health 2024; 24:686. [PMID: 38439001 PMCID: PMC10913443 DOI: 10.1186/s12889-024-18212-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/25/2024] [Indexed: 03/06/2024] Open
Abstract
BACKGROUND With the development of the economy, public health has become increasingly important. Therefore, it is important to establish a comprehensive and scientific the public health level index (PHL) system to measure public health level as a research priority. The current research has limitations in exploring the PHL system; therefore, the field still lacks a comprehensive indicator system to measure the level of public health. Therefore, this paper aims to develop a multi-level public health index system and utilizes China as a case study to evaluate its public health status. The objective is to offer insights and recommendations for the improvement of public health initiatives in China and other regions. METHODS Utilizing data from 2011 to 2020, a comprehensive PHL was developed to encompass three vital indices: the Public Health Service Index (PHS), the Public Health Resource Index (PHR), and the Population Health Level Index (PHL). Subsequently, the PHL, PHS, PHR, and PH were meticulously calculated using a comprehensive evaluation method. Amid the current disparity between public health and economic progress, both the spatial Durbin model and the spatial lag model were finally employed to examine the influence of economic level (EL) on PHL, thus affirming the consistent reliability and accuracy of PHS. RESULTS Our findings revealed the following: (i) the PHL, PHS, and PHR indices show increasing trends in China; (ii) both EL and PHL exhibit high-high clustering and low-low clustering states; (iii) the PHL in the area has a positive spatial spillover effect on the surrounding area; (iv) EL will result in the siphoning effect of PHL; and (v) EL can enhance PHL through urbanization, PH, and PHS. CONCLUSIONS The PHL system constructed in this paper demonstrates multiple levels, pluralism, spatio-temporal comparability, and robustness. It can reflect not only the input and output of public health initiatives but also the interconnectedness and autonomy within the public health system. Therefore, it can be widely utilized in other areas of public health research.
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Affiliation(s)
- Zhengqi Wei
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, School of Public Health, Guilin Medical University, Guilin, Guangxi, 541199, China.
| | - Keke Wei
- Huazhong University of Science and Technology Tongji Medical College, WuHan, 430000, China
| | - Yan Li
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, School of Public Health, Guilin Medical University, Guilin, Guangxi, 541199, China
| | - Lijie Nie
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, School of Public Health, Guilin Medical University, Guilin, Guangxi, 541199, China
| | - Yizhuang Zhou
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health, School of Public Health, Guilin Medical University, Guilin, Guangxi, 541199, China.
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Zhou M. Human Resources Allocation of the Centers for Disease Control and Prevention in China. Risk Manag Healthc Policy 2024; 17:341-353. [PMID: 38384729 PMCID: PMC10880459 DOI: 10.2147/rmhp.s452475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/08/2024] [Indexed: 02/23/2024] Open
Abstract
Objective To analyze the equity of human resources allocation of the Centers for Disease Control and Prevention (CDCs) and to predict the development in the next five years in China, and to provide a scientific basis for promoting the development of human resources. Methods The data of the CDCs from 2017 to 2021 were obtained from the "China Health Statistical Yearbook", and descriptive analysis, health resource density index (HRDI), Theil index, and health resource agglomeration degree (HRAD) were used to evaluate the equity, and the grey prediction model GM (1, 1) was used to predict the development from 2022 to 2026. Results Measured by the HRDI, the shortage of human resources in the western region was relatively obvious, with a shortage of more than 11,656 health technicians, more than 6418 licensed (assistant) physicians, and more than 693 registered nurses. The Theil index of human resources allocation by population was between 0.016 and 0.071, and the Theil index of human resources allocation by geography was between 0.312 and 0.359. The allocation of human resources by geography was more unequal than those allocated by population. In terms of HRAD, human resources are over-allocated equitably by geography in the eastern and central regions, while they are under-allocated equitably by geography in the western region. In terms of the difference between the HRAD and PAD, the eastern region has a shortage of human resources relative to the concentration population, and the western region has an excess of human resources relative to the concentration population. Conclusion The human resources allocation of the CDCs in China was uneven. The human resources of the CDCs were allocated more equitably by population than by geography. There was a situation where the equity of human resource allocation of the CDCs was contrary to the actual demand for medical care.
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Affiliation(s)
- Minghua Zhou
- Department of Administration Office, Luzhou People’s Hospital, Luzhou, Sichuan, People’s Republic of China
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Chen J, Wang S, Han Y, Zhang Y, Li Y, Zhang B, Li X, Zhang J. Geodetector analysis of individual and joint impacts of natural and human factors on maternal and child health at the provincial scale. Sci Rep 2024; 14:1643. [PMID: 38238587 PMCID: PMC10796915 DOI: 10.1038/s41598-024-52282-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 01/16/2024] [Indexed: 01/22/2024] Open
Abstract
This ecological study examined the individual and joint impacts of natural-human factors on the spatial patterns of maternal and child health status in China at the provincial scale in 2020. We considered natural factors (forest coverage, average temperature, and total sulfur dioxide and particulate matter emissions) and human factors (economic development, urbanization, healthcare access, and education level). We combined maternal, infant, and under-five mortality rates into a composite maternal and child health index using the entropy method. The spatial autocorrelation analysis of this index highlighted distinct health patterns across provinces, whereas the geodetector method assessed the effects of natural-human factors on the patterns. A notable east-central-west stepwise decline in health status was observed. Global Moran's I showed positive spatial clustering, with high-high clustering areas in the Yangtze River Delta and low-low clustering areas in western regions. Factor detection identified eight significant natural-human factors impacting maternal and child health, with total sulfur dioxide emission density having the greatest impact. The interaction between average schooling years and total sulfur dioxide emission notably affected maternal and child health patterns. The study concludes that natural-human factors critically affect the spatial distribution of maternal and child health.
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Affiliation(s)
- Jialu Chen
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Shuyuan Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Ying Han
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Yongjin Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Yuansheng Li
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Beibei Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Xiang Li
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Junhui Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China.
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Wang Z, Lin T, Xing X, Cai B, Chen Y. Dynamic distribution, regional differences and convergence of health workforce allocation in township health centers in China. Heliyon 2024; 10:e23857. [PMID: 38192759 PMCID: PMC10772724 DOI: 10.1016/j.heliyon.2023.e23857] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/10/2024] Open
Abstract
This study evaluated the dynamic distribution, regional differences, and convergence of health workforce allocation in Township Health Centers in China during 2011-2020 using data obtained from the China Health Statistics Yearbook (2012-2021). The Gini coefficient and kernel density estimation were chosen to examine the dynamic distribution and regional differences in health workforce allocation in Township Health Centers in China and their components. σ convergence and β convergence were used to investigate the change trend of health workforce allocation in Township Health Centers. The results show that between 2011 and 2020, the number of licensed doctors and registered nurses per thousand population in Township Health Centers both increased largely and regional disparities still exist. In 2020, the largest differences in the density of licensed doctors and registered nurses were found in the eastern and central regions, respectively, and the intensity of trans-variation contributed the most to the overall disparities. The allocation of licensed doctors and registered nurses both exhibited σ convergence, absolute and conditional β convergence, indicating that the regional differences in health workforce in THCs among provinces will decrease. The growth of healthcare workforce was positively impacted by the urbanization rate, growth rate of government health expenditures and growth domestic product per capita, but negatively impacted by population density in rural areas and fiscal self-sufficiency.
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Affiliation(s)
- Zuobao Wang
- School of Humanities and Law, Northeastern University, Shenyang, 110169, China
| | - Tianrun Lin
- School of Humanities and Law, Northeastern University, Shenyang, 110169, China
| | - Xinyi Xing
- School of Humanities and Law, Northeastern University, Shenyang, 110169, China
| | - Bingshu Cai
- School of Humanities and Law, Northeastern University, Shenyang, 110169, China
| | - Yao Chen
- School of Management, Shenyang Urban Construction University, Shenyang, 110167, China
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16
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Chen X, Zhou J, Yuan Q, Zhang R, Huang C, Li Y. Challenge of ending TB in China: tuberculosis control in primary healthcare sectors under integrated TB control model-a systematic review and meta-analysis. BMC Public Health 2024; 24:163. [PMID: 38212753 PMCID: PMC10785344 DOI: 10.1186/s12889-023-16292-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 07/11/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND China has the third-largest burden of tuberculosis (TB) cases in the world with great challenges towards ending TB. Primary health care (PHC) sectors play a critical role in TB prevention and control in communities under the Chinese integrated TB control model. However, there is a lack of comprehensive review of research evidence on TB control in PHC sectors under the integrated TB control model in China. METHODS This review was conducted following the PRISMA guidelines. Articles published from 2012 to January 2022 were searched from four international and three Chinese databases. Studies conducted inside mainland China and relevant with TB control service in PHC sectors under the integrated model were included. After study selection, data extraction, and quality assessment, the meta-analysis was performed with RevMan using a random-effect model.When I2 was more than 50%, subgroup analysis was performed to explore possible reasons for heterogeneity. We also conducted a post hoc sensitivity analysis for outcomes after meta-analysis by exclusion of studies with a high risk of bias or classified as low quality. RESULTS Forty-three studies from 16 provinces/municipalities in China were included in this review, and most studies included were of medium quality. PHC sectors in East China delivered TB control service better overall than that in West China, especially in tracing of patients and TB case management (TCM). In meta-analyses, both the pooled arrival rate of tracing and pooled TCM rate in East China were higher than those in West China. TB patients had a low degree of willingness to receive TCM provided by healthcare workers in PHC sectors nationwide, especially among migrant TB patients. There were 9 studies reporting factors related to TB control service in PHC sectors, 6 (2 in East and 4 in West China) of which indentified several characteristics of patients as associated factors. The context of PHC sectors was demonstrated to influence delivery of TB control service in PHC sectors in 5 studies (3 in East, 1 in Middle and 1 in West China). Most studies on strategies to promoting TB control services in PHC sectors were conducted in East China and some of these studies identified several online and offline interventions and strategies improving patients' treatment compliance [pooled OR (95% CI): 7.81 (3.08, 19.19] and awareness of TB [pooled OR (95% CI): 6.86 (2.16, 21.72)]. CONCLUSION It is of urgent need to improve TB control in PHC sector in China, particularly in West China. Formative and implementation research with rigorous design are necessary to develop comprehensive, context-specific, and patient-centered TB control strategies to promote ending TB in China.
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Affiliation(s)
- Xi Chen
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), No. 30 Gaotanyan Road, Shapingba District, Chongqing, 400038, China
- Army Medical University (Third Military Medical University), Chongqing, China
| | - Jiani Zhou
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), No. 30 Gaotanyan Road, Shapingba District, Chongqing, 400038, China
| | - Quan Yuan
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), No. 30 Gaotanyan Road, Shapingba District, Chongqing, 400038, China
| | - Rui Zhang
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), No. 30 Gaotanyan Road, Shapingba District, Chongqing, 400038, China
| | - Chunji Huang
- Army Medical University (Third Military Medical University), Chongqing, China.
| | - Ying Li
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), No. 30 Gaotanyan Road, Shapingba District, Chongqing, 400038, China.
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Chen H, Zhao L, Yu J. Spatiotemporal evolution of healthcare service capacity at township health centers in China. Front Public Health 2023; 11:1229453. [PMID: 38145066 PMCID: PMC10739490 DOI: 10.3389/fpubh.2023.1229453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023] Open
Abstract
Introduction This study analyzes the efficiency, spatiotemporal evolution, and influencing factors of provincial township health centers' healthcare service capacity in China. Method It utilizes an unexpected output super-efficiency slacks-based measure (SBM) model, exploratory spatiotemporal data analysis methods, and a quantile regression model. Results The results show that the healthcare service capacity of township health centers is better in provinces with a larger proportion of hierarchical diagnoses and treatments pilot projects in cities, and the regional efficiency trend is ordered central > eastern > western > northeastern. The healthcare service capacity of provincial township health centers mainly shows significant spatial correlation and a spatiotemporal distribution pattern of "high agglomeration, low differentiation." Discussion Rural population density and per capita GDP significantly improve the healthcare service capacity of township health centers, while local governments' healthcare and health expenditure increases the healthcare service capacity of township health centers in certain quantiles. The urbanization rate and per capita disposable income inhibit the improvement of the healthcare service capacity of township health centers in certain quantiles. The provinces should accelerate the promotion of hierarchical diagnoses and treatment pilot projects in cities and establish national cooperative development models to promote public health.
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Affiliation(s)
| | - Liyang Zhao
- College of Public Administration and Law, Hunan Agricultural University, Changsha, Hunan, China
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18
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Yip W, Fu H, Jian W, Liu J, Pan J, Xu D, Yang H, Zhai T. Universal health coverage in China part 1: progress and gaps. Lancet Public Health 2023; 8:e1025-e1034. [PMID: 38000882 DOI: 10.1016/s2468-2667(23)00254-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/26/2023]
Abstract
Over the past 2 decades, China has made remarkable progress in health-care service coverage, especially in the areas of reproductive, maternal, newborn, and child health, infectious diseases, and service capacity and access. In these areas, coverage is comparable to those in high-income countries. Inequalities of service coverage in these areas have been reduced. However, there remain large gaps in the service coverage of chronic diseases. There has been little progress in controlling risk factors of chronic diseases in the past 10 years. Service coverage for most chronic conditions is lower than in high-income countries. Moreover, China has disproportionately high incidences of catastrophic health expenditure compared with countries with similar economic development. This paper comprehensively evaluates China's progress towards universal health coverage by identifying the achievements and gaps in service coverage and financial risk protection that are crucial to achieve universal health coverage goals by 2030.
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Affiliation(s)
- Winnie Yip
- Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Hongqiao Fu
- Department of Health Policy and Management, School of Public Health, Peking University Health Science Center, Beijing, China.
| | - Weiyan Jian
- Department of Health Policy and Management, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Jue Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Jay Pan
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; School of Public Administration, Sichuan University, China
| | - Duo Xu
- Institute of Population and Labor Economics, Chinese Academy of Social Sciences, Beijing, China
| | - Hanmo Yang
- Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Tiemin Zhai
- China National Health Development Research Center, Beijing, China
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Wang X, Zhang D. Choices of medical institutions and associated factors in older patients with multimorbidity in stabilization period in China: A study based on logistic regression and decision tree model. HEALTH CARE SCIENCE 2023; 2:359-369. [PMID: 38938623 PMCID: PMC11080791 DOI: 10.1002/hcs2.73] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/21/2023] [Accepted: 09/27/2023] [Indexed: 06/29/2024]
Abstract
Background As China's population ages, its disease spectrum is changing, and the coexistence of multiple chronic diseases has become the norm with respect to the health status of its elderly population. However, the health institution choices of older patients with multimorbidity in stabilization period remains underresearched. This study investigate the factors influencing the choices of older patients with multimorbidity to provide references for the rational allocation of healthcare resources. Methods A multistage, stratified, whole-group random-sampling method was used to select eligible older patients from September to December of 2022 who attended the Community Health Service Center of Guangdong Province. We adopted a self-designed questionnaire to collect patients' general, disease-related, social-support information, their intention to choose a healthcare provider. A binary logistic regression and decision tree model based on the Chi-squared automatic interaction detector algorithm were implemented to analyze the associated factors involved. Results A total of 998 patients in stabilization period were included in the study, of which 593 (59.42%) chose hospital and 405 (40.58%) chose primary care. Our binary logistic regression results revealed that age, sex, individual average annual income, educational level, self-reported health status, activities of daily living, alcohol consumption, family doctor contracting, and family supervision of medication or exercise were the principal factors influencing the choice of medical institutions for older patients with multimorbidity (p < 0.05). The decision-tree model reflected three levels and 11 nodes, and we screened a total of four influencing factors: activities of daily living, age, a family doctor contract, and patient sex. The data showed that the logistic regression model possessed an accuracy of 72.9% and that the decision tree model exhibited an accuracy of 68.7%. Prediction using the binary logistic regression was thus statistically superior to the categorical decision-tree model based on the Chi-squared automatic interaction detector algorithm (Z = 3.238, p = 0.001). Conclusion More than half of older patients with multimorbidity in stabilization period chose hospitals for healthcare. Efforts should be made to improve the quality of healthcare services and increase the medical contracting rate and recognition of family doctors so as to attract older patients with multimorbidity to primary medical institutions.
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Affiliation(s)
- Xiaoran Wang
- Institute of Hospital Management, Shenzhen International Graduate SchoolTsinghua UniversityShenzhenChina
| | - Dan Zhang
- Institute of Hospital Management, Shenzhen International Graduate SchoolTsinghua UniversityShenzhenChina
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20
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Wang Z, Dong L, Xing X, Liu Z, Zhou Y. Disparity in hospital beds' allocation at the county level in China: an analysis based on a Health Resource Density Index (HRDI) model. BMC Health Serv Res 2023; 23:1293. [PMID: 37996897 PMCID: PMC10668462 DOI: 10.1186/s12913-023-10266-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 11/01/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND As approximately 3/4 of the population lives in county-level divisions in China, the allocation of health resources at the county level will affect the realization of health equity. This study aims to evaluate the disparity in hospital beds at the county level in China, analyze its causes, and discuss measures to optimize the allocation. METHODS Data were drawn from the Chinese County/City Statistical Yearbook (2001-2020). The health resource density index (HRDI) was applied to mediate between the influence of demographic and geographical factors on the allocation of hospital beds. The trends of HRDI allocation were evaluated through the growth incidence curve and the probability density function. The regional disparity in the HRDI was examined through the Lorenz curve, and Dagum Gini coefficient. The contribution of the Gini coefficient and its change were assessed by using the Dagum Gini decomposition method. RESULTS From 2000 to 2019, the number of hospital beds per thousand people at the county level in China increased dramatically by 1.49 times. From the aspect of the HRDI, there were large regional disparities at the national level, with a Gini coefficient of 0.367 in 2019 and in the three subregions. In 2019, the Gini coefficient of the HRDI exhibited regional variations, with the highest value observed in the western region, followed by the central region and the eastern region. Decomposition reveals that the contribution of interregional disparity changed from the dominant factor to the least important factor, accounting for 29.79% of the overall disparity and the contribution of trans-variation intensity increased from 29.19% to 39.75%, whereas the intraregional disparity remained stable at approximately 31% and became the second most important factor. CONCLUSION The regional disparity in hospital beds allocation at the county level in China was large and has not improved substantially. Trans-variation intensity was the main reason for the overall disparity and changes, and the intraregional disparity was more important than the interregional disparity for the overall disparity.
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Affiliation(s)
- Zuobao Wang
- School of Humanities and Law, Northeastern University, 195 Chuangxin Road, Hunnan District, Shenyang, 110169, Liaoning Province, China.
| | - Lin Dong
- School of Humanities and Law, Northeastern University, 195 Chuangxin Road, Hunnan District, Shenyang, 110169, Liaoning Province, China
| | - XinYi Xing
- School of Humanities and Law, Northeastern University, 195 Chuangxin Road, Hunnan District, Shenyang, 110169, Liaoning Province, China
| | - Zhe Liu
- School of Humanities and Law, Northeastern University, 195 Chuangxin Road, Hunnan District, Shenyang, 110169, Liaoning Province, China
| | - Yuxiang Zhou
- School of Humanities and Law, Northeastern University, 195 Chuangxin Road, Hunnan District, Shenyang, 110169, Liaoning Province, China.
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21
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Li Y, Wang P, Shao X, Peng F, Lv D, Du H, Wang Y, Wang X, Wu F, Chen C. Asthma prevalence based on the Baidu index and China's Health Statistical Yearbook from 2011 to 2020 in China. Front Public Health 2023; 11:1221852. [PMID: 37869190 PMCID: PMC10586501 DOI: 10.3389/fpubh.2023.1221852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 09/04/2023] [Indexed: 10/24/2023] Open
Abstract
Background Due to environmental pollution, changes in lifestyle, and advancements in diagnostic technology, the prevalence of asthma has been increasing over the years. Although China has made early efforts in asthma epidemiology and prevention, there is still a lack of unified and comprehensive epidemiological research within the country. The objective of the study is to determine the nationwide prevalence distribution of asthma using the Baidu Index and China's Health Statistical Yearbook. Methods Based on China's Health Statistical Yearbook, we analyzed the gender and age distribution of asthma in China from 2011 to 2020, as well as the length of hospitalization and associated costs. By utilizing the Baidu Index and setting the covering all 31 provinces and autonomous regions in China, we obtained the Baidu Index for the keyword 'asthma'. Heatmaps and growth ratios described the prevalence and growth of asthma in mainland China. Results The average expenditure for discharged asthma (standard deviation) patients was ¥5,870 (808). The average length of stay (standard deviation) was 7.9 (0.38) days. During the period of 2011 to 2020, hospitalization expenses for asthma increased while the length of hospital stay decreased. The proportion of discharged patients who were children under the age of 5 were 25.3% (2011), 19.4% (2012), 16% (2013), 17.9% (2014), 13.9% (2015), 11.3% (2016), 10.2% (2017), 9.4% (2018), 8.1% (2019), and 7.2% (2020), respectively. The prevalence of asthma among boys was higher than girls before the age of 14. In contrast, the proportion of women with asthma was larger than men after the age of 14. During the period from 2011 to 2020, the median [The first quartile (Q1)-the third quartile (Q3)] daily asthma Baidu index in Guangdong, Beijing, Jiangsu, Sichuan, and Zhejiang were 419 (279-476), 328 (258-376), 315 (227-365), 272 (166-313), and 312 (233-362) respectively. Coastal regions showed higher levels of attention toward asthma, indicating a higher incidence rate. Since 2014, there has been a rapid increase in the level of attention toward asthma, with the provinces of Qinghai, Sichuan, and Guangdong experiencing the fastest growth. Conclusion There are regional variations in the prevalence of asthma among different provinces in China, and the overall prevalence of asthma is increasing.
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Affiliation(s)
- Yahui Li
- First Clinical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ping Wang
- Shandong Academy of Chinese Medicine, Jinan, China
| | - Xuekun Shao
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Fulai Peng
- Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China
| | - Danyang Lv
- Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China
| | - Haitao Du
- Shandong Academy of Chinese Medicine, Jinan, China
| | - Yi Wang
- Shandong Academy of Chinese Medicine, Jinan, China
| | - Xingchen Wang
- First Clinical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Fengxia Wu
- School of Basic Medical Sciences, Shandong University, Jinan, China
| | - Cai Chen
- Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China
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Su W, Hou Y, Huang M, Xu J, Du Q, Wang P. Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approach. BMC Health Serv Res 2023; 23:995. [PMID: 37715162 PMCID: PMC10503195 DOI: 10.1186/s12913-023-09979-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 08/28/2023] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND Primary health care (PHC) institutions are key to realizing the main functions of the health care system. Since the new health care reform in 2009, the Chinese government has invested heavily in PHC institutions and launched favorable initiatives to improve the efficiency of such institutions. This study is designed to gauge the efficiency of PHC institutions by using 2012-2020 panel data covering 31 provinces in China. METHODS This study applied an improved three-stage data envelopment analysis (DEA) model to evaluate the efficiency of PHC institutions in China. Unlike the traditional three-stage DEA model, the input-oriented global super-efficiency slack-based measurement (SBM) DEA model is used to calculate the efficiency in the first and third stages of the improved three-stage DEA model, which not only allows the effects of environmental factors and random noise to be taken into account but also deal with the problem of slack, super-efficiency and the comparability of interperiod efficiency values throughout the efficiency measurement. RESULTS The results show that the efficiency of PHC institutions has been overestimated due to the impact of external environmental factors and random noise. From 2012 to 2020, the efficiency of PHC institutions displayed a downward trend. Moreover, there are significant differences in the efficiency of PHC institutions between regions, with the lowest efficiency being found in the northeast region. The efficiency of PHC institutions is significantly affected by residents' annual average income, per capita GDP, population density, the percentage of the population aged 0-14, the percentage of the population aged 65 and older, the number of people with a college education and above per 100,000 residents, and the proportion of the urban population. CONCLUSIONS Substantial investment in PHC institutions has not led to the expected efficiency gains. Therefore, more effective measures should be taken to improve the efficiency of PHC institutions in China based on local conditions. This study provides a new analytical approach to calculating the efficiency of PHC institutions, and this approach can be applied to efficiency evaluation either in other fields or in other countries.
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Affiliation(s)
- Wanmin Su
- General Practice Center, The Seventh Affiliated Hospital, Southern Medical University, Foshan, 528244, Guangdong, People's Republic of China
- School of Nursing and Health, Henan University, Kaifeng, 475004, Henan, People's Republic of China
| | - Yatian Hou
- School of Nursing and Health, Henan University, Kaifeng, 475004, Henan, People's Republic of China
| | - Mengge Huang
- School of Nursing and Health, Henan University, Kaifeng, 475004, Henan, People's Republic of China
| | - Jiamian Xu
- General Practice Center, The Seventh Affiliated Hospital, Southern Medical University, Foshan, 528244, Guangdong, People's Republic of China
| | - Qingfeng Du
- General Practice Center, The Seventh Affiliated Hospital, Southern Medical University, Foshan, 528244, Guangdong, People's Republic of China.
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, Guangdong, People's Republic of China.
| | - Peixi Wang
- General Practice Center, The Seventh Affiliated Hospital, Southern Medical University, Foshan, 528244, Guangdong, People's Republic of China.
- School of Nursing and Health, Henan University, Kaifeng, 475004, Henan, People's Republic of China.
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Gong H, Wang X, Zhang T, Li J, Chen B. How Can China's New Health Care Reform Promote the Balance of Interest Game?-Based on Game Evolution and Simulation Analysis. Risk Manag Healthc Policy 2023; 16:1435-1454. [PMID: 37575683 PMCID: PMC10422683 DOI: 10.2147/rmhp.s422296] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 07/20/2023] [Indexed: 08/15/2023] Open
Abstract
Purpose The new round of medical reform is a significant exploration of reform in the public service sector in China. Health insurance regulatory departments, medical institutions, and patients, as critical stakeholders in China's medical reform, play a crucial role in the success of the reform through their strategic interactions. Patients and Methods Starting from the perspective of bounded rationality, applies evolutionary game theory to establish an evolutionary game model for the collaborative governance of health insurance regulatory departments, medical institutions, and patients and analyzes the stability of each party's strategy and the sensitivity of parameters in the tripartite game system. Results The study shows that an equilibrium point will be formed when medical institutions provide reasonable treatment, patients choose to accept treatment, and health insurance regulatory departments adopt a lenient regulatory strategy, maximizing the interests of all parties involved in the game. Factors such as the benefits of unreasonable treatment by medical institutions, fines, and regulatory costs impact the decision-making of health insurance regulatory departments. To maximize social welfare, health insurance regulatory departments should reform payment methods, adjust medical service behaviors of medical institutions, and guide the rational allocation of medical resources; the government should increase subsidies for the operation of medical institutions and the intensity of penalties; regulatory departments should reduce regulatory costs and introduce third-party forces to strengthen health insurance supervision further. Conclusion The research findings of this paper will provide valuable insights into some countries' medical and health reform.
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Affiliation(s)
- Hanxiang Gong
- Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, People’s Republic of China
- The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, People’s Republic of China
| | - Xi Wang
- Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, People’s Republic of China
| | - Tao Zhang
- Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, People’s Republic of China
| | - Jinghua Li
- School of Public Health, Guangzhou Medical University, Guangzhou, Guangdong, People’s Republic of China
| | - Baoxin Chen
- Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, People’s Republic of China
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Kou R, Mei K, Bi Y, Huang J, Yang S, Chen K, Li W. Equity and trends in general practitioners' allocation in China: based on ten years of data from 2012 to 2021. HUMAN RESOURCES FOR HEALTH 2023; 21:61. [PMID: 37533104 PMCID: PMC10394803 DOI: 10.1186/s12960-023-00841-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 07/05/2023] [Indexed: 08/04/2023]
Abstract
BACKGROUND General practitioners (GP) are the gatekeepers of residents' health, 2021 is the 10th year of the establishment of the GP system in China. This study aims to assess the equity and trends of GP allocation in China from 2012 to 2021, summarize the efforts and progress of GPs in China during the decade, predict the development trend of GPs in mainland China in the next 5 years to provide a reference for regional health planning and rational allocation of GPs in China. METHODS Data from 2012 to 2021 on GPs in 22 provinces, 5 autonomous regions, and 4 municipalities directly under the central government in mainland China (excluding Hong Kong, Macao, and Taiwan) are collected by us. Gini coefficient, Lorenz curve and health resource agglomeration degree (HRAD) were used to analyze the equity of the allocation of GPs in China from different dimensions, a Grey prediction model was used to forecast the number of GPs in 2022-2026. RESULTS The number of GPs in mainland China increased from 109 794 to 434 868 from 2012 to 2021, with 3.08 GPs per 10 000 people in 2021. The Gini coefficient of GPs allocation by population in China decreased from 0.312 to 0.147 from 2012 to 2021, while the Gini coefficient of geographic dimension remained between 0.700 and 0.750. Compared with the degree of curvature of the Lorenz curve in the geographic dimension, the degree of curvature of the population and economic dimension were smaller. In 2021, the HRAD in the Eastern region was 4.618, the Central region was 1.493, with different degrees of imbalance among regions, the HRAD/PAD (population agglomeration degree) in the Eastern, Central and Western regions were 1.196, 0.880 and 0.821, respectively. GPs in the Eastern region is still concentrated, while the Central and Western regions were at a similar level, GPs were more scarce. The GM (1,1) model predicts that the number of GPs in mainland China will reach about 720 000 in 2026, the number of GPs per 10 000 people will reach 4.9. CONCLUSION After a decade of development, the number of GPs in China has increased significantly. It has reached the goal of the GP system when it was first established. However, the equity of the geographical dimension, both in terms of Gini coefficient and HRAD, has great differences between different regions. The average Gini coefficient at the geographic dimension is 0.723. The average HRAD index was 4.969 in the East and 0.293 in the West. The Western region has the problem of insufficient GP allocation in both population and geographical dimension. In the future, the number of GPs in China will continue to grow rapidly with the support of policies. The "2030" goal, proposed in 2018, is expected to be achieved by 2026. Due to certain factors (such as COVID-19), the actual situation may be different from the predicted results.
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Affiliation(s)
- Ruxin Kou
- School of Public Health, Weifang Medical University, Weifang, 261021, Shandong, China
| | - Kangni Mei
- School of Public Health, Weifang Medical University, Weifang, 261021, Shandong, China
| | - Yuqing Bi
- School of Public Health, Weifang Medical University, Weifang, 261021, Shandong, China
| | - Jingwen Huang
- School of Public Health, Weifang Medical University, Weifang, 261021, Shandong, China
| | - Shilan Yang
- School of Public Health, Weifang Medical University, Weifang, 261021, Shandong, China
| | - Kexuan Chen
- School of Public Health, Weifang Medical University, Weifang, 261021, Shandong, China
| | - Wei Li
- School of Public Health, Weifang Medical University, Weifang, 261021, Shandong, China.
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Wang Z, Ye W, Chen X, Li Y, Zhang L, Li F, Yao N, Gao C, Wang P, Yi D, Wu Y. Spatio-temporal pattern, matching level and prediction of ageing and medical resources in China. BMC Public Health 2023; 23:1155. [PMID: 37322467 PMCID: PMC10268402 DOI: 10.1186/s12889-023-15945-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 05/20/2023] [Indexed: 06/17/2023] Open
Abstract
OBJECTIVE Population ageing, as a hot issue in global development, increases the burden of medical resources in society. This study aims to assess the current spatiotemporal evolution and interaction between population ageing and medical resources in mainland China; evaluate the matching level of medical resources to population ageing; and forecast future trends of ageing, medical resources, and the indicator of ageing-resources (IAR). METHODS Data on ageing (EPR) and medical resources (NHI, NBHI, and NHTP) were obtained from China Health Statistics Yearbook and China Statistical Yearbook (2011-2020). We employed spatial autocorrelation to examine the spatial-temporal distribution trends and analyzed the spatio-temporal interaction using a Bayesian spatio-temporal effect model. The IAR, an improved evaluation indicator, was used to measure the matching level of medical resources to population ageing with kernel density analysis for visualization. Finally, an ETS-DNN model was used to forecast the trends in population ageing, medical resources, and their matching level over the next decade. RESULTS The study found that China's ageing population and medical resources are growing annually, yet distribution is uneven across districts. There is a spatio-temporal interaction effect between ageing and medical resources, with higher levels of both in Eastern China and lower levels in Western China. The IAR is relatively high in Northwest, North China, and the Yangtze River Delta, but showed a declining trend in North China and the Yangtze River Delta. The hybrid model (ETS-DNN) gained an R2 of 0.9719, and the predicted median IAR for 2030 (0.99) across 31 regions was higher than the median IAR for 2020 (0.93). CONCLUSION This study analyzes the relationship between population ageing and medical resources, revealing a spatio-temporal interaction between them. The IAR evaluation indicator highlights the need to address ageing population challenges and cultivate a competent health workforce. The ETS-DNN forecasts indicate higher concentrations of both medical resources and ageing populations in eastern China, emphasizing the need for region-specific ageing security systems and health service industries. The findings provide valuable policy insights for addressing a hyper-aged society in the future.
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Affiliation(s)
- Zhenyan Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Wei Ye
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Xicheng Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Yang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Ling Zhang
- Department of Health Education, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Fang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Ning Yao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Chengcheng Gao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Pengyu Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Dong Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
| | - Yazhou Wu
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
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Chen L, Zeng H, Wu L, Tian Q, Zhang N, He R, Xue H, Zheng J, Liu J, Liang F, Zhu B. Spatial Accessibility Evaluation and Location Optimization of Primary Healthcare in China: A Case Study of Shenzhen. GEOHEALTH 2023; 7:e2022GH000753. [PMID: 37200630 PMCID: PMC10187614 DOI: 10.1029/2022gh000753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/20/2023] [Accepted: 04/04/2023] [Indexed: 05/20/2023]
Abstract
The unbalanced allocation of healthcare resources is a major challenge that hinders access to healthcare. Taking Shenzhen as an example, this study aimed to enhance equity in obtaining healthcare services, through measuring and visualizing the spatial accessibility of community healthcare centers (CHC), and optimizing CHC geospatial allocation. We used the number of health technicians per 10,000 to represent the CHC's service capacity, combined with resident points and census data to calculate the population the CHC needs to carry, and then analyzed the accessibility based on the Gaussian two-step floating catchment area method. In 2020, five regions in Shenzhen had better spatial accessibility scores: Nanshan (0.250), Luohu (0.246), Futian (0.244), Dapeng (0.226), and Yantian (0.196). The spatial accessibility of CHCs shows a gradual decrease from the city center to the edge, which is affected by economic and topographic factors. With the support of the maximal covering location problem model, we selected up to 567 candidate locations for the new CHC, which could improve Shenzhen's accessibility score from 0.189 to 0.361 and increase the coverage population by 63.46% within a 15-min impedance. By introducing spatial techniques and maps, this study provides (a) new evidence for promoting equitable access to primary healthcare services in Shenzhen and (b) a foundation for improving the accessibility of public service facilities in other areas.
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Affiliation(s)
- Liutong Chen
- School of Public Health and Emergency ManagementSouthern University of Science and TechnologyShenzhenChina
| | - Huatang Zeng
- Shenzhen Health Development Research and Data Management CenterShenzhenChina
| | - Liqun Wu
- Shenzhen Health Development Research and Data Management CenterShenzhenChina
| | - Qiannan Tian
- Shenzhen Health Development Research and Data Management CenterShenzhenChina
| | - Ning Zhang
- School of Public Policy and AdministrationXi'an Jiaotong UniversityXi'anChina
| | - Rongxin He
- Vanke School of Public HealthTsinghua UniversityBeijingChina
| | - Hao Xue
- Stanford Center on China's Economy and InstitutionsStanford UniversityStanfordCAUSA
| | - Junyao Zheng
- China Institute for Urban GovernanceShanghai Jiao Tong UniversityShanghaiChina
- School of International and Public AffairsShanghai Jiao Tong UniversityShanghaiChina
| | - Jinlin Liu
- School of Public Policy and AdministrationNorthwestern Polytechnical UniversityXi'anChina
| | - Fengchao Liang
- School of Public Health and Emergency ManagementSouthern University of Science and TechnologyShenzhenChina
| | - Bin Zhu
- School of Public Health and Emergency ManagementSouthern University of Science and TechnologyShenzhenChina
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Zhang M, Bao Y, Yang Y, Kimber M, Levine M, Xie F. Identifying Attributes for a Value Assessment Framework in China: A Qualitative Study. PHARMACOECONOMICS 2023; 41:439-455. [PMID: 36729295 PMCID: PMC9893981 DOI: 10.1007/s40273-022-01235-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/20/2022] [Indexed: 05/10/2023]
Abstract
BACKGROUND Value assessment frameworks (VAFs) are promising tools for measuring the value of health technologies and informing coverage policymaking; however, most published VAFs were developed for high-income countries. This study aimed to identify value attributes as part of the development of a VAF in China. METHODS We used the qualitative description approach. Specifically, we conducted open-ended semi-structured interviews with Chinese stakeholders, as well as a review and analysis of publicly available government documents related to health technology assessment (HTA) and coverage policies in China. Conventional content analysis and the constant comparison technique were used to generate value attributes. Multiple criteria were used to determine the inclusion of a value attribute, with response levels of included attributes finalized via consensus meetings among the research team. RESULTS Thirty-four stakeholders living or working in China completed the semi-structured interview. These stakeholders included policymakers (n = 4), healthcare providers (n = 8), HTA researchers (n = 6), patients and members of the general public (n = 9), and industry representatives (n = 7). In addition, 16 government documents were included for analysis. Twelve value attributes grouped in eight categories are included in the VAF: (1) severity of disease; (2) health benefit, including survival, clinical outcomes, and patient-reported outcomes; (3) safety; (4) economic impact, including budget impact to payer and patients, and cost effectiveness; (5) innovation; (6) organizational impact; (7) health equity; and (8) quality of evidence. CONCLUSION These 12 value attributes were identified for the development of a VAF to support health technologies' value assessment and coverage policymaking in China.
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Affiliation(s)
- Mengmeng Zhang
- Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
| | - Yun Bao
- Institute of Clinical Research and Evidence-Based Medicine, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Yi Yang
- Key Lab of Health Technology Assessment, National Health Commission, Fudan University, Shanghai, China
| | - Melissa Kimber
- Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
- Offord Centre for Child Studies, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, L8L 0A4, Canada
| | - Mitchell Levine
- Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
- Division of Clinical Pharmacology and Toxicology, Department of Medicine,, McMaster University, Hamilton, ON, L8N 4A6, Canada
| | - Feng Xie
- Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
- Centre for Health Economics and Policy Analysis (CHEPA), McMaster University, Hamilton, ON, L8S 4K1, Canada.
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Otero-Leon DF, Lavieri MS, Denton BT, Sussman J, Hayward RA. Monitoring policy in the context of preventive treatment of cardiovascular disease. Health Care Manag Sci 2023; 26:93-116. [PMID: 36284034 DOI: 10.1007/s10729-022-09621-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 10/11/2022] [Indexed: 11/04/2022]
Abstract
Preventing chronic diseases is an essential aspect of medical care. To prevent chronic diseases, physicians focus on monitoring their risk factors and prescribing the necessary medication. The optimal monitoring policy depends on the patient's risk factors and demographics. Monitoring too frequently may be unnecessary and costly; on the other hand, monitoring the patient infrequently means the patient may forgo needed treatment and experience adverse events related to the disease. We propose a finite horizon and finite-state Markov decision process to define monitoring policies. To build our Markov decision process, we estimate stochastic models based on longitudinal observational data from electronic health records for a large cohort of patients seen in the national U.S. Veterans Affairs health system. We use our model to study policies for whether or when to assess the need for cholesterol-lowering medications. We further use our model to investigate the role of gender and race on optimal monitoring policies.
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Affiliation(s)
- Daniel F Otero-Leon
- Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Mariel S Lavieri
- Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Brian T Denton
- Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jeremy Sussman
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Rodney A Hayward
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
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Luo Y, Shi Z, Guo D, He P. Toward Universal Health Coverage: Regional Inequalities and Potential Solutions for Alleviating Catastrophic Health Expenditure in the Post-poverty Elimination Era of China. Int J Health Policy Manag 2023; 12:7332. [PMID: 37579440 PMCID: PMC10125131 DOI: 10.34172/ijhpm.2023.7332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 01/02/2023] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND This study took Beijing as an example to estimate the incidence and regional inequalities of catastrophic health expenditures (CHE) in a megacity of China. METHODS This study used data from the Health Services Survey Beijing (HSSB) 2018. Logistic regressions were used to investigate the risk factors for experiencing CHE, and concentration curves, the concentration index and its decomposition method based on probit models were used to estimate the inequalities in CHE. RESULTS CHE occurred in 25.51% of the households of the outer suburb villages, 6.78% of the households of the inner-city area communities, 17.10% of the households of the villages of the inner-city areas, and 11.91% of the households of the communities of the outer suburbs. In areas in the outer suburbs, households with private insurance coverage were associated with a lowered risk of CHE, and lower educational attainment and lower occupational class were related to an increasing risk of CHE. This study also discovered pro-rich financing disparities in CHE in Beijing, with the outer suburbs having the highest levels of CHE disparity. When it comes to the observed contributions of disparities in CHE, a significant portion of them is connected to the sorts of occupations, educational levels, and residential status. CONCLUSION The impoverishment brought on by medical expenses and CHE must still be taken into account in the post-poverty elimination era. The megacity of China was discovered to have significant regional differences in the incidence of pro-rich financing inequity in CHE. Disparities in socioeconomic status (SES), one of the controllable variables, may be a key area to address to lower the risk and minimize CHE inequality in megacities towards the path to UHC. Additionally, it is important to consider the financial protection impact of inclusive supplementary medical insurance on lowering the likelihood of CHE in the periphery areas.
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Affiliation(s)
- Yanan Luo
- Department of Global Health, School of Public Health, Peking University, Beijing, China
| | - Zhenyu Shi
- School of Public Health, Peking University, Beijing, China
- China Center for Health Development Studies, Peking University, Beijing, China
| | - Dan Guo
- School of Public Health, Peking University, Beijing, China
- China Center for Health Development Studies, Peking University, Beijing, China
| | - Ping He
- China Center for Health Development Studies, Peking University, Beijing, China
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Yuan L, Cao J, Wang D, Yu D, Liu G, Qian Z. Regional disparities and influencing factors of high quality medical resources distribution in China. Int J Equity Health 2023; 22:8. [PMID: 36627636 PMCID: PMC9832614 DOI: 10.1186/s12939-023-01825-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 01/02/2023] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND With the gradual increase of residents' income and the continuous improvement of medical security system, people's demand for pursuing higher quality and better medical and health services has been released. However, so far little research has been published on China's high quality medical resources (HQMR). This study aims to understand the spatiotemporal variation trend of HQMR from 2006 to 2020, analyze regional disparity of HQMR in 2020, and further explore the main factors influencing the distribution of HQMR in China. METHODS The study selected Class III level A hospitals (the highest level medical institutions in China) to represent HQMR. Descriptive statistical methods were used to address the changes in the distribution of HQMR from 2006 to 2020. Lorentz curve, Gini coefficient (G), Theil index (T) and High-quality health resource density index (HHRDI) were used to calculate the degree of inequity. The geographical detector method was used to reveal the key factors influencing the distribution of HQMR. RESULTS The total amount of HQMR in China had increased year by year, from 647 Class III level A hospitals in 2006 to 1580 in 2020. In 2020, G for HQMR by population was 0.166, while by geographic area was 0.614. T was consistent with the results for G, and intra-regional contribution rates were higher than inter-regional contribution rates. HHRDI showed that Beijing, Shanghai, and Tianjin had the highest allocated amounts of HQMR. The results of the geographical detector showed that total health costs, government health expenditure, size of resident populations, GDP, number of medical colleges had a significant impact on the spatial distribution of HQMR and the q values were 0.813, 0.781, 0.719, 0.661, 0.492 respectively. There was an interaction between the influencing factors. CONCLUSIONS China's total HQMR is growing rapidly but is relatively inadequate. The distribution of HQMR by population is better than by geography, and the distribution by geography is less equitable. Population size and geographical area both need to be taken into account when formulating policies, rather than simply increasing the number of HQMR.
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Affiliation(s)
- Lei Yuan
- grid.452223.00000 0004 1757 7615Xiangya Hospital, Central South University, Changsha, Hunan China ,grid.452223.00000 0004 1757 7615National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan China
| | - Jing Cao
- grid.431010.7Department of Cardiovascular Medicine, Third Xiangya Hospital, Central South University, Changsha, Hunan China
| | - Dong Wang
- grid.452223.00000 0004 1757 7615Xiangya Hospital, Central South University, Changsha, Hunan China
| | - Dan Yu
- grid.452223.00000 0004 1757 7615Xiangya Hospital, Central South University, Changsha, Hunan China ,grid.452223.00000 0004 1757 7615National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan China
| | - Ge Liu
- grid.452223.00000 0004 1757 7615Xiangya Hospital, Central South University, Changsha, Hunan China
| | - Zhaoxin Qian
- grid.452223.00000 0004 1757 7615Xiangya Hospital, Central South University, Changsha, Hunan China ,grid.452223.00000 0004 1757 7615National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan China
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Sun J, Sun Z, Yang C, Shen Y. Estimating the Distribution and Convergence of County-level Healthcare Resources Allocative Efficiency in China Based on DEA and Spatial Panel Model. INQUIRY : A JOURNAL OF MEDICAL CARE ORGANIZATION, PROVISION AND FINANCING 2023; 60:469580231178122. [PMID: 37300427 DOI: 10.1177/00469580231178122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Although China's 2009 New Healthcare Reform aimed to correct the imbalance in the spatial allocation of healthcare resources with a focus on the county level, its impact on county-level allocative efficiency evolution and convergence remains unclear. This paper for the first time performs a spatial analysis to explore the distribution, evolution, and convergence of the allocative efficiency of healthcare resources with county-level data. This paper uses the sample data of 158 countries in Henan Province, China, to evaluate the evolution and convergence of the allocative efficiency of healthcare resources. Based on the estimated Data Envelopment Analysis (DEA) allocative efficiency, analysis of variance (ANOVA), and spatial descriptive analysis, we explore the county heterogeneity and efficiency evolution; a spatial panel model is then utilized to test the county-level convergence of the allocative efficiency of healthcare resources. Although the number of efficient counties has not increased, the number of inefficient individuals keeps decreasing, and the allocative efficiency of municipal districts is lower than that of nonmunicipal counties. There exists a positive spatial correlation of allocative efficiency in Henan Province, and significant and robust convergence results can be found at the county level after China's 2009 reform. This study reveals a diversified picture of China's county-level spatial evolution of allocative efficiency in healthcare resources, showing a more balanced spatial distribution of allocative efficiency since the triggering of China's 2009 reform. However, long-term investment incentives and a targeted allocation of healthcare resources are still needed to promote further efficiency convergence and increase the number of counties with efficiency.
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Yang C, Cui D, Yin S, Wu R, Ke X, Liu X, Yang Y, Sun Y, Xu L, Teng C. Fiscal autonomy of subnational governments and equity in healthcare resource allocation: Evidence from China. Front Public Health 2022; 10:989625. [PMID: 36249207 PMCID: PMC9561467 DOI: 10.3389/fpubh.2022.989625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/08/2022] [Indexed: 01/26/2023] Open
Abstract
Objectives Promoting equity in healthcare resource allocation (EHRA) has become a critical political agenda of governments at all levels since the ambitious Universal Health Coverage was launched in China in 2009, while the role of an important institutional variable-fiscal autonomy of subnational governments-is often overlooked. The present study was designed to determine the effect of FASG on EHRA and its potential mechanism of action and heterogeneity characteristics to provide empirical support for the research field expansion and relative policies making of EHRA. Methods From the start, we utilized the Theil index and the entropy method to calculate the EHRA index of 22 provinces (2011-2020) based on the medical resource data of 287 prefecture-level cities. Furthermore, we used the two-way fixed effects model (FE) to identify and analyze the impact of FASG on EHRA and then used three robustness test strategies and two-stage least squares (2SLS) regression to verify the reliability of the conclusions and deal with potential endogeneity problems, respectively. At last, we extend the baseline regression model and obtain the two-way FE threshold model for conducting heterogeneity analysis, which makes us verify whether the baseline model has nonlinear characteristics. Results The static value and the trend of interannual changes in the EHRA values in different provinces are both very different. The regression results of the two-way FE model show that FASG has a significant positive impact on EHRA, and the corresponding estimated coefficient is - 0.0849 (P < 0.01). Moreover, this promotion effect can be reflected through two channels: enhancing the intensity of government health expenditure (IGHE) and optimizing the allocation of human resources for health (AHRH). At last, under the different economic and demographic constraints, the impact of FASG on EHRA has nonlinear characteristics, i.e., after crossing a specific threshold of per capita DGP (PGDP) and population density (PD), the promotion effect is reduced until it is not statistically significant, while after crossing a particular threshold of dependency ratio (DR), the promotion effect is further strengthened and still statistically significant. Conclusions FASG plays an essential role in promoting EHRA, which shows that subnational governments need to attach great importance to the construction of fiscal capability in the allocation of health care resources, effectively improve the equity of medical and health fiscal expenditures, and promote the sustainable improvement of the level of EHRA.
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Affiliation(s)
- Ciran Yang
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, China,Global Health Institute, Wuhan University, Wuhan, China
| | - Dan Cui
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, China,Global Health Institute, Wuhan University, Wuhan, China,*Correspondence: Dan Cui
| | - Shicheng Yin
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, China,Global Health Institute, Wuhan University, Wuhan, China
| | - Ruonan Wu
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, China,Global Health Institute, Wuhan University, Wuhan, China
| | - Xinfeng Ke
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, China,Global Health Institute, Wuhan University, Wuhan, China
| | - Xiaojun Liu
- Public Health School, Fujian Medical University, Fuzhou, China
| | - Ying Yang
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, China,Global Health Institute, Wuhan University, Wuhan, China
| | - Yixuan Sun
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, China,Global Health Institute, Wuhan University, Wuhan, China
| | - Luxinyi Xu
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, China,Global Health Institute, Wuhan University, Wuhan, China
| | - Caixia Teng
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, China,Global Health Institute, Wuhan University, Wuhan, China
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Song C, Jiang ZQ, Hu LF, Li WH, Liu XL, Wang YY, Jin WY, Zhu ZW. A machine learning-based diagnostic model for children with autism spectrum disorders complicated with intellectual disability. Front Psychiatry 2022; 13:993077. [PMID: 36213933 PMCID: PMC9533131 DOI: 10.3389/fpsyt.2022.993077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/01/2022] [Indexed: 11/18/2022] Open
Abstract
Background Early detection of children with autism spectrum disorder (ASD) and comorbid intellectual disability (ID) can help in individualized intervention. Appropriate assessment and diagnostic tools are lacking in primary care. This study aims to explore the applicability of machine learning (ML) methods in diagnosing ASD comorbid ID compared with traditional regression models. Method From January 2017 to December 2021, 241 children with ASD, with an average age of 6.41 ± 1.96, diagnosed in the Developmental Behavior Department of the Children's Hospital Affiliated with the Medical College of Zhejiang University were included in the analysis. This study trained the traditional diagnostic models of Logistic regression (LR), Support Vector Machine (SVM), and two ensemble learning algorithms [Random Forest (RF) and XGBoost]. Socio-demographic and behavioral observation data were used to distinguish whether autistic children had combined ID. The hyperparameters adjustment uses grid search and 10-fold validation. The Boruta method is used to select variables. The model's performance was evaluated using discrimination, calibration, and decision curve analysis (DCA). Result Among 241 autistic children, 98 (40.66%) were ASD comorbid ID. The four diagnostic models can better distinguish whether autistic children are complicated with ID, and the accuracy of SVM is the highest (0.836); SVM and XGBoost have better accuracy (0.800, 0.838); LR has the best sensitivity (0.939), followed by SVM (0.952). Regarding specificity, SVM, RF, and XGBoost performed significantly higher than LR (0.355). The AUC of ML (SVM, 0.835 [95% CI: 0.747-0.944]; RF, 0.829 [95% CI: 0.738-0.920]; XGBoost, 0.845 [95% CI: 0.734-0.937]) is not different from traditional LR (0.858 [95% CI: 0.770-0.944]). Only SVM observed a good calibration degree. Regarding DCA, LR, and SVM have higher benefits in a wider threshold range. Conclusion Compared to the traditional regression model, ML model based on socio-demographic and behavioral observation data, especially SVM, has a better ability to distinguish whether autistic children are combined with ID.
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Affiliation(s)
- Chao Song
- Department of Developmental and Behavioral Pediatrics, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Centre for Child Health, Hangzhou, China
| | | | - Li-Fei Hu
- Department of Developmental and Behavioral Pediatrics, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Centre for Child Health, Hangzhou, China
| | - Wen-Hao Li
- Department of Developmental and Behavioral Pediatrics, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Centre for Child Health, Hangzhou, China
| | - Xiao-Lin Liu
- Department of Developmental and Behavioral Pediatrics, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Centre for Child Health, Hangzhou, China
| | - Yan-Yan Wang
- Department of Developmental and Behavioral Pediatrics, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Centre for Child Health, Hangzhou, China
| | - Wen-Yuan Jin
- Department of Developmental and Behavioral Pediatrics, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Centre for Child Health, Hangzhou, China
| | - Zhi-Wei Zhu
- Department of Developmental and Behavioral Pediatrics, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Centre for Child Health, Hangzhou, China
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Bai Q, Ke X, Huang L, Liu L, Xue D, Bian Y. Finding flaws in the spatial distribution of health workforce and its influential factors: An empirical analysis based on Chinese provincial panel data, 2010-2019. Front Public Health 2022; 10:953695. [PMID: 36589992 PMCID: PMC9794860 DOI: 10.3389/fpubh.2022.953695] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022] Open
Abstract
Background The maldistributions of the health workforce showed great inconsistency when singly measured by population quantity or geographic area in China. Meanwhile, earlier studies mainly employed traditional econometric approaches to investigate determinants for the health workforce, which ignored spillover effects of influential factors on neighboring regions. Therefore, we aimed to analyze health workforce allocation in China from demographic and geographic perspectives simultaneously and then explore the spatial pattern and determinants for health workforce allocation taking account of the spillover effect. Methods The health resource density index (HRDI) equals the geometric mean of health resources per 1,000 persons and per square kilometer. First, the HRDI of licensed physicians (HRDI_P) and registered nurses (HRDI_N) was calculated for descriptive analysis. Then, global and local Moran's I indices were employed to explore the spatial features and aggregation clusters of the health workforce. Finally, four types of independent variables were selected: supportive resources (bed density and government health expenditure), healthcare need (proportion of the elderly population), socioeconomic factors (urbanization rate and GDP per capita), and sociocultural factors (education expenditure per pupil and park green area per capita), and then the spatial panel econometric model was used to assess direct associations and intra-region spillover effects between independent variables and HRDI_P and HRDI_N. Results Global Moran's I index of HRDI_P and HRDI_N increased from 0.2136 (P = 0.0070) to 0.2316 (P = 0.0050), and from 0.1645 (P = 0.0120) to 0.2022 (P = 0.0080), respectively. Local Moran's I suggested spatial aggregation clusters of HRDI_P and HRDI_N. For HRDI_P, bed density, government health expenditure, and GDP had significantly positive associations with local HRDI_P, while the proportion of the elderly population and education expenditure showed opposite spillover effects. More precisely, a 1% increase in the proportion of the elderly population would lead to a 0.4098% increase in HRDI_P of neighboring provinces, while a 1% increase in education expenditure leads to a 0.2688% decline in neighboring HRDI_P. For HRDI_N, the urbanization rate, bed density, and government health expenditure exerted significantly positive impacted local HRDI_N. In addition, the spillover effect was more evident in the urbanization rate, with a 1% increase in the urbanization rate relating to 0.9080% growth of HRDI_N of surrounding provinces. Negative spillover effects of education expenditure, government health expenditure, and elderly proportion were observed in neighboring HRDI_N. Conclusion There were substantial spatial disparities in health workforce distribution in China; moreover, the health workforce showed positive spatial agglomeration with a strengthening tendency in the last decade. In addition, supportive resources, healthcare needs, and socioeconomic and sociocultural factors would affect the health labor configuration not only in a given province but also in its nearby provinces.
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Affiliation(s)
- Qian Bai
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Macau, China
| | - Xinyu Ke
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Macau, China
| | - Lieyu Huang
- Office of Policy and Planning Research, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Liming Liu
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Dongmei Xue
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Macau, China
| | - Ying Bian
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Macau, China
- *Correspondence: Ying Bian
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Wang H, Sun H, Jin C, Wang M, Luo Y, Song W, Wang H. Preference to Family Doctor Contracted Service of Patients with Chronic Disease in Urban China: A Discrete Choice Experiment. Patient Prefer Adherence 2022; 16:2103-2114. [PMID: 35989974 PMCID: PMC9384844 DOI: 10.2147/ppa.s371188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 08/02/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE Shanghai is one of the pioneers proposing family doctor contract service (FDCS). However, there is no quantitative research focusing on the Shanghai experience from a demand-side perspective. This study investigated Shanghai chronic patients' relative preferences for FDCS using a discrete choice experiment method. METHODS A face-to-face discrete choice experiment (DCE) was performed to elicit the preference with 300 samples. Attributes and levels were extracted from the literature review and focus group consultation with patients. Seven attributes, follow-up frequency, medicine accessibility, family doctor competency, health management, referral convenience, appointment flexibility, and shared decision-making, were decided. Three levels were attached to each attribute. A mixed logit model was used to evaluate the multiple-choice data. RESULTS A total of 248 patients completed the survey. Patient valued FDCS medicine accessibility (β=0.57, P < 0.05), and high family doctor competency (β= 0.43, P < 0.05), regular health management activities (β=0.36, P < 0.05), high follow-up frequency (β=0.31, P < 0.05) the most. The good doctor-patient shared decision-making atmosphere (β=0.12, P < 0.05), high referral convenience (β=0.06, P < 0.05) and high appointment flexibility (β=0.04, P < 0.05) are valued as less important. No significant preference heterogeneity was identified for patients with different sociodemographic characteristics. Respondents reported other FDCS needs, including online health consultation, specialist services in local institutes, higher reimbursement rates, free rehabilitation guidance for the disabled and personal health management. CONCLUSION This research is the first discrete choice experiment FDCS preference research targeting on Chinese urban population. The results suggested that to increase the quality of FDCS, policy-makers should prioritize follow-up frequency, medicine accessibility, family doctor competency and health management. The service package should consider a higher reimbursement rate and rehabilitation guidance for the disabled if extra health-care resources available. Future FDCS policy should consider stated societal preference and be congruent with it.
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Affiliation(s)
- Haode Wang
- Shanghai Health Development Research Center, Shanghai Medical Information Center, Shanghai, People’s Republic of China
| | - Hui Sun
- Shanghai Health Development Research Center, Shanghai Medical Information Center, Shanghai, People’s Republic of China
- Key Laboratory of Health Technology Assessment, National Health Commission; School of Public Health, Fudan University, Shanghai, People’s Republic of China
| | - Chunlin Jin
- Shanghai Health Development Research Center, Shanghai Medical Information Center, Shanghai, People’s Republic of China
| | - Meifeng Wang
- Shanghai Health Development Research Center, Shanghai Medical Information Center, Shanghai, People’s Republic of China
| | - Yashuang Luo
- Shanghai Health Development Research Center, Shanghai Medical Information Center, Shanghai, People’s Republic of China
| | - Wenqian Song
- Shanghai Overseas Rescue and Aid Service Center, Shanghai, People’s Republic of China
| | - Haiyin Wang
- Shanghai Health Development Research Center, Shanghai Medical Information Center, Shanghai, People’s Republic of China
- Correspondence: Haiyin Wang, Shanghai Health Development Research Center, Minhang District, No. 181 Xinbei Road, Shanghai, 201199, People’s Republic of China, Tel +86- 18917769216, Email
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