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      Spatial equity and factors associated with intensive care unit bed allocation in China

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          Abstract

          Background

          At present, unequal allocation of medical resources represents a major problem for medical service management in China and many other countries. Equity of intensive care unit (ICU) bed allocation is essential for timely and equitable access to medical care for critically ill patients. This study analysed the equity of ICU bed allocation in 31 provincial regions in China, and the associated factors, to provide a theoretical basis for improvement in the allocation of ICU beds.

          Methods

          The equity of ICU bed allocation was investigated in 31 provincial regions in China in 2021. The Gini coefficient combined with Lorenz curves were used to analyse the current status of ICU bed allocation by both population and service area. The spatial heterogeneity and aggregation of ICU bed density were analysed using the Global Moran’s index. The spatial distribution pattern was visualized via LISA maps using the Local Moran’s index. Three grey correlation models were constructed to assess the key factors influencing ICU bed density. Finally, robustness analysis was performed to test the reliability of the results.

          Results

          The allocation of ICU beds in China was highly inequitable by service area (Gini = 0.68) and showed better balance by population distribution (Gini = 0.14). The distribution of ICU beds by service area was highly spatially clustered (Global Moran’s I = 0.22). The bed utilization rate exhibited the strongest association with ICU bed density by population. Registered nurses per 10,000 square kilometres was the strongest factor affecting ICU bed density by service area.

          Conclusions

          The allocation of ICU beds by population is better than by service area; the allocation by service area is less equitable in China. These findings emphasise the need to implement better measures to reduce ICU bed equity differences between regions and balance and coordinate medical resources. Service area size, bed utilization, the number of registered nurses and other key factors should be considered when performing regional health planning for ICU bed supply. This will increase the equitable access to critical medical services for all populations.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13690-024-01402-5.

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          Most cited references48

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          Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area

          There is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19).
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            Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy

            In December 2019, a novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) emerged in China and has spread globally, creating a pandemic. Information about the clinical characteristics of infected patients who require intensive care is limited.
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              The variability of critical care bed numbers in Europe.

              To quantify the numbers of critical care beds in Europe and to understand the differences in these numbers between countries when corrected for population size and gross domestic product. Prospective data collection of critical care bed numbers for each country in Europe from July 2010 to July 2011. Sources were identified in each country that could provide data on numbers of critical care beds (intensive care and intermediate care). These data were then cross-referenced with data from international databases describing population size and age, gross domestic product (GDP), expenditure on healthcare and numbers of acute care beds. We identified 2,068,892 acute care beds and 73,585 (2.8 %) critical care beds. Due to the heterogeneous descriptions of these beds in the individual countries it was not possible to discriminate between intensive care and intermediate care in most cases. On average there were 11.5 critical care beds per 100,000 head of population, with marked differences between countries (Germany 29.2, Portugal 4.2). The numbers of critical care beds per country corrected for population size were positively correlated with GDP (r(2) = 0.16, p = 0.05), numbers of acute care beds corrected for population (r(2) = 0.12, p = 0.05) and the percentage of acute care beds designated as critical care (r(2) = 0.59, p < 0.0001). They were not correlated with the proportion of GDP expended on healthcare. Critical care bed numbers vary considerably between countries in Europe. Better understanding of these numbers should facilitate improved planning for critical care capacity and utilization in the future.
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                Author and article information

                Contributors
                xche77@163.com
                Journal
                Arch Public Health
                Arch Public Health
                Archives of Public Health
                BioMed Central (London )
                0778-7367
                2049-3258
                29 September 2024
                29 September 2024
                2024
                : 82
                : 169
                Affiliations
                [1 ]School of Public Health, Wuhan University of Science and Technology, ( https://ror.org/00e4hrk88) Wuhan, China
                [2 ]Department of Nursing, School of Medicine, Wuhan University of Science and Technology, ( https://ror.org/00e4hrk88) Wuhan, China
                Article
                1402
                10.1186/s13690-024-01402-5
                11439290
                39343936
                3d880524-c07c-403c-8918-1fb27ea7b3e6
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

                History
                : 4 January 2024
                : 18 September 2024
                Funding
                Funded by: Humanities and Social Sciences Research Planning Fund Program of the Ministry of Education of China
                Award ID: 23YJAZH166
                Funded by: Health Commission of Hubei Province scientific research project
                Award ID: WJ2023Z009
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Public health
                intensive care unit (icu),bed allocation,gini coefficient,grey correlation model,equity

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