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      Health resource allocation in Western China from 2014 to 2018

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          Abstract

          Background

          Health equity has persistently been a global concern. How to fairly and appropriately allocate health resources is a research hotspot. While Western China is relatively backward economically and presents difficulties for the allocation of health resources, little attention has been given to the equity of resource allocation there. This study analysed the equity of allocation of beds, physicians and nurses in Western China from 2014-2018 to provide targeted guidance for improving the equity of health resource allocation.

          Methods

          Data for 2014-2018 obtained from the Statistical Yearbook (2015-2019) of provinces (autonomous regions and municipalities) were used to analyse health resource allocation in terms of beds, physicians and nurses in Western China. The Lorenz curve and Gini coefficient were calculated to evaluate equity in the population dimension and geographic dimension. The Theil index was used to measure the inequity of the three indicators between minority and nonminority areas.

          Results

          The number of beds, physicians and nurses in Western China showed an increasing trend from 2014-2018. The Lorenz curve had a smaller curvature in the population dimension than in the geographic dimension. The Gini coefficients for health resources in the population dimension ranged from 0.044 to 0.079, and in the geographic dimension, the Gini coefficients ranged between 0.614 and 0.647. The above results showed that the equity of health resource allocation was better in the population dimension than in the geographic dimension. The Theil index ranged from 0.000 to 0.004 in the population dimension and from 0.095 to 0.326 in the geographic dimension, indicating that the inequity in health resource allocation was higher in the geographic dimension. The intergroup contribution ratios of the Theil index in both the population and geographic dimensions were greater than 60%, indicating that the inequity in resource allocation was mainly caused by intergroup differences, namely, the allocation of health resources within the province. Among them, the inequity of physicians and nurses allocation was the most obvious.

          Conclusions

          From 2014 to 2018, the total amount of health resources have improved in Western China. However, health resource allocation in Western China was still inequitable in the population and geographic dimensions, and the inequity of health resource allocation in the geographic dimension showed a tendency to worsen. Meanwhile, although the inequity of human recourse allocation was alleviated in 2018 compare with 2014, the inequity of human resource distribution within provinces was still obvious. The government can increase the number of health resources and improve the accessibility of health resources by increasing financial investment, strengthening humanistic care for health workers, and establishing internet hospitals.

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          Most cited references34

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          On the measurement of inequalities in health.

          This paper offers a critical appraisal of the various methods employed to date to measure inequalities in health. It suggests that only two of these--the slope index of inequality and the concentration index--are likely to present an accurate picture of socioeconomic inequalities in health. The paper also presents several empirical examples to illustrate of the dangers of using other measures such as the range, the Lorenz curve and the index of dissimilarity.
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            Measuring the availability of human resources for health and its relationship to universal health coverage for 204 countries and territories from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019

            (2022)
            Background Human resources for health (HRH) include a range of occupations that aim to promote or improve human health. The UN Sustainable Development Goals (SDGs) and the WHO Health Workforce 2030 strategy have drawn attention to the importance of HRH for achieving policy priorities such as universal health coverage (UHC). Although previous research has found substantial global disparities in HRH, the absence of comparable cross-national estimates of existing workforces has hindered efforts to quantify workforce requirements to meet health system goals. We aimed to use comparable and standardised data sources to estimate HRH densities globally, and to examine the relationship between a subset of HRH cadres and UHC effective coverage performance. Methods Through the International Labour Organization and Global Health Data Exchange databases, we identified 1404 country-years of data from labour force surveys and 69 country-years of census data, with detailed microdata on health-related employment. From the WHO National Health Workforce Accounts, we identified 2950 country-years of data. We mapped data from all occupational coding systems to the International Standard Classification of Occupations 1988 (ISCO-88), allowing for standardised estimation of densities for 16 categories of health workers across the full time series. Using data from 1990 to 2019 for 196 of 204 countries and territories, covering seven Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) super-regions and 21 regions, we applied spatiotemporal Gaussian process regression (ST-GPR) to model HRH densities from 1990 to 2019 for all countries and territories. We used stochastic frontier meta-regression to model the relationship between the UHC effective coverage index and densities for the four categories of health workers enumerated in SDG indicator 3.c.1 pertaining to HRH: physicians, nurses and midwives, dentistry personnel, and pharmaceutical personnel. We identified minimum workforce density thresholds required to meet a specified target of 80 out of 100 on the UHC effective coverage index, and quantified national shortages with respect to those minimum thresholds. Findings We estimated that, in 2019, the world had 104·0 million (95% uncertainty interval 83·5–128·0) health workers, including 12·8 million (9·7–16·6) physicians, 29·8 million (23·3–37·7) nurses and midwives, 4·6 million (3·6–6·0) dentistry personnel, and 5·2 million (4·0–6·7) pharmaceutical personnel. We calculated a global physician density of 16·7 (12·6–21·6) per 10 000 population, and a nurse and midwife density of 38·6 (30·1–48·8) per 10 000 population. We found the GBD super-regions of sub-Saharan Africa, south Asia, and north Africa and the Middle East had the lowest HRH densities. To reach 80 out of 100 on the UHC effective coverage index, we estimated that, per 10 000 population, at least 20·7 physicians, 70·6 nurses and midwives, 8·2 dentistry personnel, and 9·4 pharmaceutical personnel would be needed. In total, the 2019 national health workforces fell short of these minimum thresholds by 6·4 million physicians, 30·6 million nurses and midwives, 3·3 million dentistry personnel, and 2·9 million pharmaceutical personnel. Interpretation Considerable expansion of the world's health workforce is needed to achieve high levels of UHC effective coverage. The largest shortages are in low-income settings, highlighting the need for increased financing and coordination to train, employ, and retain human resources in the health sector. Actual HRH shortages might be larger than estimated because minimum thresholds for each cadre of health workers are benchmarked on health systems that most efficiently translate human resources into UHC attainment. Funding Bill & Melinda Gates Foundation.
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              National equity of health resource allocation in China: data from 2009 to 2013

              Background The inequitable allocation of health resources is a worldwide problem, and it is also one of the obstacles facing for health services utilization in China. A new round of health care reform which contains the important aspect of improving the equity in health resource allocation was released by Chinese government in 2009. The aim of this study is to understand the changes of equity in health resource allocation from 2009 to 2013, and make a further inquiry of the main factors which influence the equity conditions in China. Methods Data resources are the China Health Statistics Yearbook (2014) and the China Statistical Yearbook (2014). Four indicators were chosen to measure the trends in equity of health resource allocation. Data were disaggregated by three geographical regions: west, central, and east. Theil index was used to calculate the degree of unfairness. Results The total amount of health care resources in China had been increasing in recent years. However, the per 10, 000 km2 number of health resources showed a huge gap in different regions, and per 10, 000 capita health resources ownership showed a relatively small disparities at the same time. The index of health resources showed an overall downward trend, in which health financial investment the most unfair from 2009 to 2012 and the number of health institutions the most unfair in 2013. The equity of health resources allocation in eastern regions was the worst except for the aspect of health technical personnel allocation. The regional contribution rates were lower than that of the inter-regional contribution rates which were all beyond 60 %. Conclusion The equity of health resource allocation improved gradually from 2009 to 2013. However, the internal differences within the eastern region still have a huge impact on the overall equity in health resource allocation. The tough issues of inequitable in health resource allocation should be resolved by comprehensive measures from a multidisciplinary perspective.
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                Author and article information

                Contributors
                wangzhenggx@outlook.com
                Hehaoyu_gxmu@outlook.com
                elsie_l@qq.com
                gxcdcwhk@163.com
                1179308390@qq.com
                583400698@qq.com
                Journal
                Arch Public Health
                Arch Public Health
                Archives of Public Health
                BioMed Central (London )
                0778-7367
                2049-3258
                22 February 2023
                22 February 2023
                2023
                : 81
                : 30
                Affiliations
                [1 ]GRID grid.256607.0, ISNI 0000 0004 1798 2653, School of Information and Management, , Guangxi Medical University, ; 22 Shuangyong Road, Nanning, 530021 Guangxi China
                [2 ]GRID grid.256607.0, ISNI 0000 0004 1798 2653, Guangxi Medical College, ; 8 Kunlun Road, Nanning, 530000 Guangxi China
                [3 ]GRID grid.256607.0, ISNI 0000 0004 1798 2653, Quality Management Department, , the Affiliated Hospital of Stomatology, Guangxi Medical University, ; 10 Shuangyong Road, Nanning, 530021 Guangxi China
                [4 ]Maternal and Child Health Care Hospital of Guangxi Zhuang Autonomous Region, 225 Xinyang Road, Nanning, 530002 Guangxi China
                Author information
                http://orcid.org/0000-0001-7499-4675
                Article
                1046
                10.1186/s13690-023-01046-x
                9946701
                36814309
                84c9da1a-21c5-4a7b-8b88-620ba2d04a6d
                © The Author(s) 2023

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 6 November 2021
                : 15 February 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100012434, Middle-aged and Young Teachers' Basic Ability Promotion Project of Guangxi;
                Award ID: 2020KY43004
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2023

                Public health
                resource allocation,regional health planning,health equity
                Public health
                resource allocation, regional health planning, health equity

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