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      Access to intensive care in 14 European countries: a spatial analysis of intensive care need and capacity in the light of COVID-19

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          Abstract

          Purpose

          The coronavirus disease 2019 (COVID-19) poses major challenges to health-care systems worldwide. This pandemic demonstrates the importance of timely access to intensive care and, therefore, this study aims to explore the accessibility of intensive care beds in 14 European countries and its impact on the COVID-19 case fatality ratio (CFR).

          Methods

          We examined access to intensive care beds by deriving (1) a regional ratio of intensive care beds to 100,000 population capita (accessibility index, AI) and (2) the distance to the closest intensive care unit. The cross-sectional analysis was performed at a 5-by-5 km spatial resolution and results were summarized nationally for 14 European countries. The relationship between AI and CFR was analyzed at the regional level.

          Results

          We found national-level differences in the levels of access to intensive care beds. The AI was highest in Germany (AI = 35.3), followed by Estonia (AI = 33.5) and Austria (AI = 26.4), and lowest in Sweden (AI = 5) and Denmark (AI = 6.4). The average travel distance to the closest hospital was highest in Croatia (25.3 min by car) and lowest in Luxembourg (9.1 min). Subnational results illustrate that capacity was associated with population density and national-level inventories. The correlation analysis revealed a negative correlation of ICU accessibility and COVID-19 CFR ( r = − 0.57; p < 0.001).

          Conclusion

          Geographical access to intensive care beds varies significantly across European countries and low ICU accessibility was associated with a higher proportion of COVID-19 deaths to cases (CFR). Important differences in access are due to the sizes of national resource inventories and the distribution of health-care facilities relative to the human population. Our findings provide a resource for officials planning public health responses beyond the current COVID-19 pandemic, such as identifying potential locations suitable for temporary facilities or establishing logistical plans for moving severely ill patients to facilities with available beds.

          Electronic supplementary material

          The online version of this article (10.1007/s00134-020-06229-6) contains supplementary material, which is available to authorized users.

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          Most cited references22

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          An interactive web-based dashboard to track COVID-19 in real time

          In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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            COVID-19 and Italy: what next?

            Summary The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. A global response to prepare health systems worldwide is imperative. Although containment measures in China have reduced new cases by more than 90%, this reduction is not the case elsewhere, and Italy has been particularly affected. There is now grave concern regarding the Italian national health system's capacity to effectively respond to the needs of patients who are infected and require intensive care for SARS-CoV-2 pneumonia. The percentage of patients in intensive care reported daily in Italy between March 1 and March 11, 2020, has consistently been between 9% and 11% of patients who are actively infected. The number of patients infected since Feb 21 in Italy closely follows an exponential trend. If this trend continues for 1 more week, there will be 30 000 infected patients. Intensive care units will then be at maximum capacity; up to 4000 hospital beds will be needed by mid-April, 2020. Our analysis might help political leaders and health authorities to allocate enough resources, including personnel, beds, and intensive care facilities, to manage the situation in the next few days and weeks. If the Italian outbreak follows a similar trend as in Hubei province, China, the number of newly infected patients could start to decrease within 3–4 days, departing from the exponential trend. However, this cannot currently be predicted because of differences between social distancing measures and the capacity to quickly build dedicated facilities in China.
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              Chronic kidney disease and mortality risk: a systematic review.

              Current guidelines identify people with chronic kidney disease (CKD) as being at high risk for cardiovascular and all-cause mortality. Because as many as 19 million Americans may have CKD, a comprehensive summary of this risk would be potentially useful for planning public health policy. A systematic review of the association between non-dialysis-dependent CKD and the risk for all-cause and cardiovascular mortality was conducted. Patient- and study-related characteristics that influenced the magnitude of these associations also were investigated. MEDLINE and EMBASE databases were searched, and reference lists through December 2004 were consulted. Authors of 10 primary studies provided additional data. Cohort studies or cohort analyses of randomized, controlled trials that compared mortality between those with and without chronically reduced kidney function were included. Studies were excluded from review when participants were followed for < 1 yr or had ESRD. Two reviewers independently extracted data on study setting, quality, participant and renal function characteristics, and outcomes. Thirty-nine studies that followed a total of 1,371,990 participants were reviewed. The unadjusted relative risk for mortality in participants with reduced kidney function compared with those without ranged from 0.94 to 5.0 and was significantly more than 1.0 in 93% of cohorts. Among the 16 studies that provided suitable data, the absolute risk for death increased exponentially with decreasing renal function. Fourteen cohorts described the risk for mortality from reduced kidney function, after adjustment for other established risk factors. Although adjusted relative hazards were consistently lower than unadjusted relative risks (median reduction 17%), they remained significantly more than 1.0 in 71% of cohorts. This review supports current guidelines that identify individuals with CKD as being at high risk for cardiovascular mortality. Determining which interventions best offset this risk remains a health priority.
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                Author and article information

                Contributors
                j.bauer@med.uni-frankfurt.de
                Journal
                Intensive Care Med
                Intensive Care Med
                Intensive Care Medicine
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0342-4642
                1432-1238
                4 September 2020
                4 September 2020
                : 1-9
                Affiliations
                [1 ]GRID grid.7839.5, ISNI 0000 0004 1936 9721, Division of Health Services Research, Institute of Occupational Medicine, Social Medicine and Environmental Medicine, , Goethe University Frankfurt, ; Theodor Stern Kai 7, 60590 Frankfurt, Germany
                [2 ]GRID grid.4567.0, ISNI 0000 0004 0483 2525, Institute of Health Economics and Health Care Management, , Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), ; Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
                [3 ]GRID grid.9018.0, ISNI 0000 0001 0679 2801, Department of Economics, , Martin Luther University Halle-Wittenberg, ; 06099 Halle an der Saale, Germany
                [4 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Nuffield Department of Medicine, Malaria Atlas Project, Big Data Institute, , University of Oxford, ; Roosevelt Drive, Oxford, OX3 7FY UK
                Author information
                http://orcid.org/0000-0001-6267-9731
                Article
                6229
                10.1007/s00134-020-06229-6
                7472675
                32886208
                d755900d-0a30-4924-98ef-fd15af644814
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial 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-nc/4.0/.

                History
                : 3 July 2020
                : 21 August 2020
                Funding
                Funded by: Johann Wolfgang Goethe-Universität, Frankfurt am Main (1022)
                Categories
                Original

                Emergency medicine & Trauma
                access,intensive care,europe,covid-19
                Emergency medicine & Trauma
                access, intensive care, europe, covid-19

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