Determinants of hospital inefficiency. The case of Polish county hospitals.

Local hospitals play a crucial role in the healthcare system. In this study, the efficiency of Polish county hospitals is assessed by considering characteristics of hospitals that may determine their performance, such as the form of ownership, size, and staff structure. The main goal was to analyze the effect of three possible determinants on efficiency: ownership, the presence of an Emergency Department, and the presence of an Intensive Care Unit. The study covered different subgroups of hospitals and different approaches of inputs and outputs. An input-oriented radial super-efficiency DEA model under variable returns to scale was used for the efficiency analysis, and then differences between distributions of efficient and inefficient units were evaluated using a Chi-square test. A Kruskal-Wallis test was also used to analyze differences in mean efficiency. Inefficiency scores were regressed with hospital characteristics to test for other determinants. These results did not confirm differences in efficiency concerning ownership. However, in some subgroups of hospitals, running an Emergency Department or an Intensive Care Unit had a significant effect. Tobit regression results provided additional insight into how an Emergency Department or Intensive Care Unit can affect efficiency. Both cases had an effect of increasing inefficiency, and the data suggested that the department/unit size plays an important role.

Allocation of Respirators in the Coronavirus Crisis in Israel: An Ethical Analysis and A Scheme for Triage.

BACKGROUND: This focus article is a theoretical reflection on the ethics of allocating respirators to patients in circumstances of shortage, especially during the coronavirus disease-2019 (COVID-19) outbreak in Israel. In this article, respirators are placeholders for similar life-saving modalities in short supply, such as extracorporeal membrane oxygenation machines and intensive care unit beds. In the article, I propose a system of triage for circumstances of scarcity of respirators. The system separates the hopeless from the curable, granting every treatable person a real chance of cure. The scarcity situation eliminates excesses of medicine, and then allocates respirators by a single scale, combining an evidence-based scoring system with risk-proportionate lottery. The triage proposed embodies continuity and consistency with the healthcare practices in ordinary times. Yet, I suggest two regulatory modifications: one in relation to expediting review of novel and makeshift solutions and the second in relation to mandatory retrospective research on all relevant medical data and standard (as opposed to experimental) interventions that are influenced by the triage.

UTI no estado de Goiás: estudo de suficiência: relatório resumido / ICU in the state of Goiás: sufficiency study: summary report

Objetivo de propiciar aos gestores a realização de uma programação de internações/leitos mais coerente com as reais necessidades da população do estado de Goiás. Estudo exploratório, com bases de dados secundários e foco no aprofundamento da percepção de determinados cálculos/medidas não demonstrados/elucidados na portaria. Período do estudo: 2014 a 2020.Sistema de Informações/Softwares Utilizados: SIH/SUS; ANS, SCNES, SINASC, Projeção populacional do IMB. Softwares utilizados: TabWin, Microsoft Office, LibreOffice, WPS Office, Google Drive, Power BI, GitLab, Java. Indicadores previstos: nº de internações e leitos esperados, gerais e de UTI. Os dados considerados neste estudo foram coletados antes do período da pandemia da Covid-19, os leitos dedicados ao enfretamento da pandemia não foram incorporados nas análises. Apresenta os principais resultados para o período analisado no Brasil e em Goiás referentes a estabelecimentos de saúde/leitos, os resultados apurados para o Estado de Goiás em relação aos leitos gerais SUS e não SUS por especialidade, leitos de UTI SUS e não SUS por especialidade, a faixa de variação de leitos gerais e de UTI/SUS preconizados pela Portaria 1.101/2002. Os leitos gerais e UTI SUS por especialidade, para Goiânia. Após os ajustes na metodologia para a obtenção dos dados necessários à implementação das fórmulas da portaria, desenvolveu-se um protótipo de simulador para identificação dos milhares de cenários possíveis para a programação de internações e leitos, gerais e de UTI, em esfera estadual, de conformidade ao exemplo contido no item 3 deste relatório

Objective of providing managers with a schedule of admissions/beds more consistent with the real needs of the population of the state of Goiás. Exploratory study, with secondary databases and focus on deepening the perception of certain calculations/measures not demonstrated/elucidated at the gatehouse. Study period: 2014 to 2020. Information System/Software Used: SIH/SUS; ANS, SCNES, SINASC, IMB population projection. Software used: TabWin, Microsoft Office, LibreOffice, WPS Office, Google Drive, Power BI, GitLab, Java. Expected indicators: number of hospitalizations and expected beds, general and ICU. The data considered in this study were collected before the period of the Covid-19 pandemic, the beds dedicated to dealing with the pandemic were not incorporated in the analyses. It presents the main results for the period analyzed in Brazil and Goiás referring to health establishments/beds, the results obtained for the State of Goiás in relation to general SUS and non-SUS beds by specialty, SUS and non-SUS ICU beds by specialty , the range of variation of general beds and ICU/SUS recommended by Ordinance 1,101/2002. General beds and SUS ICU by specialty, for Goiânia. After adjustments in the methodology to obtain the data necessary to implement the ordinance formulas, a simulator prototype was developed to identify the thousands of possible scenarios for the programming of hospitalizations and beds, general and ICU, at the state level, of conformity to the example contained in item 3 of this report

Analysis of COVID-19 Cases' Spatial Dependence in US Counties Reveals Health Inequalities.

On March 13, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) caused by the novel coronavirus SARS-CoV2 a pandemic. Since then the virus has infected over 9.1 million individuals and resulted in over 470,000 deaths worldwide (as of June 24, 2020). Here, we discuss the spatial correlation between county population health rankings and the incidence of COVID-19 cases and COVID-19 related deaths in the United States. We analyzed the spread of the disease based on multiple variables at the county level, using publicly available data on the numbers of confirmed cases and deaths, intensive care unit beds and socio-demographic, and healthcare resources in the U.S. Our results indicate substantial geographical variations in the distribution of COVID-19 cases and deaths across the US counties. There was significant positive global spatial correlation between the percentage of Black Americans and cases of COVID-19 (Moran I = 0.174 and 0.264, p < 0.0001). A similar result was found for the global spatial correlation between the percentage of Black American and deaths due to COVID-19 at the county level in the U.S. (Moran I = 0.264, p < 0.0001). There was no significant spatial correlation between the Hispanic population and COVID-19 cases and deaths; however, a higher percentage of non-Hispanic white was significantly negatively spatially correlated with cases (Moran I = -0.203, p < 0.0001) and deaths (Moran I = -0.137, p < 0.0001) from the disease. This study showed significant but weak spatial autocorrelation between the number of intensive care unit beds and COVID-19 cases (Moran I = 0.08, p < 0.0001) and deaths (Moran I = 0.15, p < 0.0001), respectively. These findings provide more detail into the interplay between the infectious disease and healthcare-related characteristics of the population. Only by understanding these relationships will it be possible to mitigate the rate of spread and severity of the disease.

Cancellation of Elective Surgery and Intensive Care Unit Capacity in New York State: A Retrospective Cohort Analysis.

BACKGROUND: In response to the coronavirus disease 2019 (COVID-19) pandemic, New York State ordered the suspension of all elective surgeries to increase intensive care unit (ICU) bed capacity. Yet the potential impact of suspending elective surgery on ICU bed capacity is unclear. METHODS: We retrospectively reviewed 5 years of New York State data on ICU usage. Descriptions of ICU utilization and mechanical ventilation were stratified by admission type (elective surgery, emergent/urgent/trauma surgery, and medical admissions) and by geographic location (New York metropolitan region versus the rest of New York State). Data are presented as absolute numbers and percentages and all adult and pediatric ICU patients were included. RESULTS: Overall, ICU admissions in New York State were seen in 10.1% of all hospitalizations (n = 1,232,986/n = 12,251,617) and remained stable over a 5-year period from 2011 to 2015. Among n = 1,232,986 ICU stays, sources of ICU admission included elective surgery (13.4%, n = 165,365), emergent/urgent admissions/trauma surgery (28.0%, n = 345,094), and medical admissions (58.6%, n = 722,527). Ventilator utilization was seen in 26.3% (n = 323,789/n = 1232,986) of all ICU patients of which 6.4% (n = 20,652), 32.8% (n = 106,186), and 60.8% (n = 196,951) was for patients from elective, emergent, and medical admissions, respectively. New York City holds the majority of ICU bed capacity (70.0%; n = 2496/n = 3566) in New York State. CONCLUSIONS: Patients undergoing elective surgery comprised a small fraction of ICU bed and mechanical ventilation use in New York State. Suspension of elective surgeries in response to the COVID-19 pandemic may thus have a minor impact on ICU capacity when compared to other sources of ICU admission such as emergent/urgent admissions/trauma surgery and medical admissions. More study is needed to better understand how best to maximize ICU capacity for pandemics requiring heavy use of critical care resources.

Early prediction of level-of-care requirements in patients with COVID-19.

This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.

The new coronavirus (now named SARS-CoV-2) causing the disease pandemic in 2019 (COVID-19), has so far infected over 35 million people worldwide and killed more than 1 million. Most people with COVID-19 have no symptoms or only mild symptoms. But some become seriously ill and need hospitalization. The sickest are admitted to an Intensive Care Unit (ICU) and may need mechanical ventilation to help them breath. Being able to predict which patients with COVID-19 will become severely ill could help hospitals around the world manage the huge influx of patients caused by the pandemic and save lives. Now, Hao, Sotudian, Wang, Xu et al. show that computer models using artificial intelligence technology can help predict which COVID-19 patients will be hospitalized, admitted to the ICU, or need mechanical ventilation. Using data of 2,566 COVID-19 patients from five Massachusetts hospitals, Hao et al. created three separate models that can predict hospitalization, ICU admission, and the need for mechanical ventilation with more than 86% accuracy, based on patient characteristics, clinical symptoms, laboratory results and chest x-rays. Hao et al. found that the patients' vital signs, age, obesity, difficulty breathing, and underlying diseases like diabetes, were the strongest predictors of the need for hospitalization. Being male, having diabetes, cloudy chest x-rays, and certain laboratory results were the most important risk factors for intensive care treatment and mechanical ventilation. Laboratory results suggesting tissue damage, severe inflammation or oxygen deprivation in the body's tissues were important warning signs of severe disease. The results provide a more detailed picture of the patients who are likely to suffer from severe forms of COVID-19. Using the predictive models may help physicians identify patients who appear okay but need closer monitoring and more aggressive treatment. The models may also help policy makers decide who needs workplace accommodations such as being allowed to work from home, which individuals may benefit from more frequent testing, and who should be prioritized for vaccination when a vaccine becomes available.

The efficiency in the ordinary hospital bed management in Italy: An in-depth analysis of intensive care unit in the areas affected by COVID-19 before the outbreak.

Since the end of February 2020 a severe diffusion of COVID-19 has affected Italy and in particular its northern regions, resulting in a high demand of hospitalizations in particular in the intensive care units (ICUs). Hospitals are suffering the high degree of patients to be treated for respiratory diseases and the majority of the health structures, especially in the north of Italy, are or are at risk of saturation. Therefore, the question whether and to what extent the reduction of hospital beds occurred in the past years has biased the management of the emergency has come to the front in the public debate. In our opinion, to start a robust analysis it is necessary to consider the Italian health system capacity prior to the emergency. Therefore, the aim of this study is to analyse the availability of hospital beds across the country as well as to determine their management in terms of complexity and performance of cases treated at regional level. The results of this study underlines that, despite the reduction of beds for the majority of the hospital wards, ICUs availabilities did not change between 2010 and 2017. Moreover, this study confirms that the majority of the Italian regions have a routinely efficient management of their facilities allowing hospitals to treat patients without the risk of having an overabundance of patients and a scarcity of beds. In fact, this analysis shows that, in normal situations, the management of hospital and ICU beds has no critical levels.

COVID-19 - The Availability of ICU Beds in Brazil during the Onset of Pandemic.

Background: Brazil faces some challenges in the battle against the COVID-19 pandemic, including: the risks for cross-infection (community infection) increase in densely populated areas; low access to health services in areas where the number of beds in intensive care units (ICUs) is scarce and poorly distributed, mainly in states with low population density. Objective: To describe and intercorrelate epidemiology and geographic data from Brazil about the number of intensive care unit (ICU) beds at the onset of COVID-19 pandemic. Methods: The epidemiology and geographic data were correlated with the distribution of ICU beds (public and private health systems) and the number of beneficiaries of private health insurance using Pearson's Correlation Coefficient. The same data were correlated using partial correlation controlled by gross domestic product (GDP) and number of beneficiaries of private health insurance. Findings: Brazil has a large geographical area and diverse demographic and economic aspects. This diversity is also present in the states and the Federal District regarding the number of COVID-19 cases, deaths and case fatality rate. The effective management of severe COVID-19 patients requires ICU services, and the scenario was also dissimilar as for ICU beds and ICU beds/10,000 inhabitants for the public (SUS) and private health systems mainly at the onset of COVID-19 pandemic. The distribution of ICUs was uneven between public and private services, and most patients rely on SUS, which had the lowest number of ICU beds. In only a few states, the number of ICU beds at SUS was above 1 to 3 by 10,000 inhabitants, which is the number recommended by the World Health Organization (WHO). Conclusions: Brazil needed to improve the number of ICU beds units to deal with COVID-19 pandemic, mainly for the SUS showing a late involvement of government and health authorities to deal with the COVID-19 pandemic.

Estimating Shortages in Capacity to Deliver Continuous Kidney Replacement Therapy During the COVID-19 Pandemic in the United States.

RATIONALE & OBJECTIVE: During the coronavirus disease 2019 (COVID-19) pandemic, New York encountered shortages in continuous kidney replacement therapy (CKRT) capacity for critically ill patients with acute kidney injury stage 3 requiring dialysis. To inform planning for current and future crises, we estimated CKRT demand and capacity during the initial wave of the US COVID-19 pandemic. STUDY DESIGN: We developed mathematical models to project nationwide and statewide CKRT demand and capacity. Data sources included the Institute for Health Metrics and Evaluation model, the Harvard Global Health Institute model, and published literature. SETTING & POPULATION: US patients hospitalized during the initial wave of the COVID-19 pandemic (February 6, 2020, to August 4, 2020). INTERVENTION: CKRT. OUTCOMES: CKRT demand and capacity at peak resource use; number of states projected to encounter CKRT shortages. MODEL, PERSPECTIVE, & TIMEFRAME: Health sector perspective with a 6-month time horizon. RESULTS: Under base-case model assumptions, there was a nationwide CKRT capacity of 7,032 machines, an estimated shortage of 1,088 (95% uncertainty interval, 910-1,568) machines, and shortages in 6 states at peak resource use. In sensitivity analyses, varying assumptions around: (1) the number of pre-COVID-19 surplus CKRT machines available and (2) the incidence of acute kidney injury stage 3 requiring dialysis requiring CKRT among hospitalized patients with COVID-19 resulted in projected shortages in 3 to 8 states (933-1,282 machines) and 4 to 8 states (945-1,723 machines), respectively. In the best- and worst-case scenarios, there were shortages in 3 and 26 states (614 and 4,540 machines). LIMITATIONS: Parameter estimates are influenced by assumptions made in the absence of published data for CKRT capacity and by the Institute for Health Metrics and Evaluation model's limitations. CONCLUSIONS: Several US states are projected to encounter CKRT shortages during the COVID-19 pandemic. These findings, although based on limited data for CKRT demand and capacity, suggest there being value during health care crises such as the COVID-19 pandemic in establishing an inpatient kidney replacement therapy national registry and maintaining a national stockpile of CKRT equipment.

A snapshot of European neurosurgery December 2019 vs. March 2020: just before and during the Covid-19 pandemic.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or Covid-19), which began as an epidemic in China and spread globally as a pandemic, has necessitated resource management to meet emergency needs of Covid-19 patients and other emergent cases. We have conducted a survey to analyze caseload and measures to adapt indications for a perception of crisis. METHODS: We constructed a questionnaire to survey a snapshot of neurosurgical activity, resources, and indications during 1 week with usual activity in December 2019 and 1 week during SARS-CoV-2 pandemic in March 2020. The questionnaire was sent to 34 neurosurgical departments in Europe; 25 departments returned responses within 5 days. RESULTS: We found unexpectedly large differences in resources and indications already before the pandemic. Differences were also large in how much practice and resources changed during the pandemic. Neurosurgical beds and neuro-intensive care beds were significantly decreased from December 2019 to March 2020. The utilization of resources decreased via less demand for care of brain injuries and subarachnoid hemorrhage, postponing surgery and changed surgical indications as a method of rationing resources. Twenty departments (80%) reduced activity extensively, and the same proportion stated that they were no longer able to provide care according to legitimate medical needs. CONCLUSION: Neurosurgical centers responded swiftly and effectively to a sudden decrease of neurosurgical capacity due to relocation of resources to pandemic care. The pandemic led to rationing of neurosurgical care in 80% of responding centers. We saw a relation between resources before the pandemic and ability to uphold neurosurgical services. The observation of extensive differences of available beds provided an opportunity to show how resources that had been restricted already under normal conditions translated to rationing of care that may not be acceptable to the public of seemingly affluent European countries.

Concerns for low-resource countries, with under-prepared intensive care units, facing the COVID-19 pandemic.

BACKGROUND: Low-resource countries with fragile healthcare systems lack trained healthcare professionals and specialized resources for COVID-19 patient hospitalization, including mechanical ventilators. Additional socio-economic complications such as civil war and financial crisis in Libya and other low-resource countries further complicate healthcare delivery. METHODS: A cross-sectional survey evaluating hospital and intensive care unit's capacity and readiness was performed from 16 leading Libyan hospitals in March 2020. In addition, a survey was conducted among 400 doctors who worked in these hospitals to evaluate the status of personal protective equipment. RESULTS: Out of 16 hospitals, the highest hospital capacity was 1000 in-patient beds, while the lowest was 25 beds with a median of 200 (IQR 52-417, range 25-1000) hospital beds. However, a median of only eight (IQR 6-14, range 3-37) available functioning ICU beds were reported in these hospitals. Only 9 (IQR 4.5-14, range 2-20) mechanical ventilators were reported and none of the hospitals had a reverse transcription-polymerase chain reaction machine for COVID-19 testing. Moreover, they relied on one of two central laboratories located in major cities. Our PPE survey revealed that 56.7% hospitals lacked PPE and 53% of healthcare workers reported that they did not receive proper PPE training. In addition, 70% reported that they were buying the PPE themselves as hospitals did not provide them. CONCLUSION: This study provides an alarming overview of the unpreparedness of Libyan hospitals for detecting and treating patients with COVID-19 and limiting the spread of the pandemic.

What if the worst consequences of COVID-19 concerned non-COVID patients?

We highlight in this short article the side-effects of COVID-19 pandemic on the management of non-COVID patients, with potential detrimental and irreversible complications. We thus propose adjusted strategies to deal with both COVID and non-COVID patients.

The COVID-19 pandemic in Brazil: analysis of supply and demand of hospital and ICU beds and mechanical ventilators under different scenarios. / Pandemia por COVID-19 no Brasil: análise da demanda e da oferta de leitos hospitalares e equipamentos de ventilação assistida segundo diferentes cenários.

This study aims to analyze the pressure on the Brazilian health system from the additional demand created by COVID-19. The authors performed a series of simulations to estimate the demand for hospital beds (health micro-regions) as well as to ICU beds, and mechanical ventilators (health macro-regions) under different scenarios of intensity (infection rates equivalent to 0.01, 0.1, and 1 case por 100 inhabitants) and time horizons (1, 3, and 6 months). The results reveal a critical situation in the system for meeting this potential demand, with numerous health micro-regions and macro-regions operating beyond their capacity, compromising the care for patients, especially those with more severe symptoms. The study presents three relevant messages. First, it is necessary to slow the spread of COVID-19 in the Brazilian population, allowing more time for the reorganization of the supply and relieve the pressure on the health system. Second, the expansion of the number of available beds will be the key. Even if the private sector helps offset the deficit, the combined supply from the two sectors (public and private) would be insufficient in various macro-regions. The construction of field hospitals is important, both in places with a history of "hospital deserts" and in those already pressured by demand. The third message involves the regionalized organization of health services, whose design may be adequate in situations of routine demand, but which suffer additional challenges during pandemics, especially if patients have to travel long distances to receive care.

O objetivo deste estudo é analisar a pressão sobre o sistema de saúde no Brasil decorrente da demanda adicional gerada pela COVID-19. Para tanto, foi realizado um conjunto de simulações para estimar a demanda de leitos gerais (microrregiões de saúde), leitos de UTI e equipamentos de ventilação assistida (macrorregiões de saúde) em diferentes cenários, para intensidade (taxas de infecção equivalentes a 0,01, 0,1 e 1 caso por 100 habitantes) e horizontes temporais (1, 3 e 6 meses). Os resultados evidenciam uma situação crítica do sistema para atender essa demanda potencial, uma vez que diversas microrregiões e macrorregiões de saúde operariam além de sua capacidade, comprometendo o atendimento a pacientes principalmente aqueles com sintomas mais severos. O estudo apresenta três mensagens relevantes. Em primeiro lugar, é necessário reduzir a velocidade de propagação da COVID-19 na população brasileira, permitindo um tempo maior para a reorganização da oferta e aliviando a pressão sobre o sistema de saúde. Segundo, é necessário expandir o número de leitos disponíveis. Ainda que o setor privado contribua para amortecer o déficit de demanda, a oferta conjunta dos dois setores não seria suficiente em várias macrorregiões. A construção de hospitais de campanha é importante, tanto em locais onde historicamente há vazios assistenciais como também naqueles onde já se observa uma pressão do lado da demanda. A terceira mensagem diz respeito à organização regionalizada dos serviços de saúde que, apesar de adequada em situações de demanda usual, em momentos de pandemia este desenho implica desafios adicionais, especialmente se a distância que o paciente tiver de percorrer for muito grande.

El objetivo de este estudio es analizar la presión sobre el sistema de salud brasileño, ocasionada por la demanda adicional de camas hospitalarias y equipos de ventilación mecánica, generada por el COVID-19. Para tal fin, se realizó un conjunto de simulaciones, con el fin de estimar la demanda de camas generales (microrregiones de salud), camas de UTI y equipamientos de ventilación asistida (macrorregiones de salud) en diferentes escenarios, según la intensidad (tasas de infección equivalentes a 0,01, 0,1 y 1 caso por 100 habitantes) y horizontes temporales (1, 3 y 6 meses). Los resultados evidencian una situación crítica del sistema para atender esa demanda potencial, ya que diversas microrregiones y macrorregiones de salud operarían más allá de su capacidad, comprometiendo la atención a pacientes principalmente aquellos con los síntomas más graves. El estudio presenta tres mensajes relevantes. En primer lugar, es necesario reducir la velocidad de propagación del COVID-19 en la población brasileña, permitiendo un tiempo mayor para la reorganización de la oferta y aliviando la presión sobre el sistema de salud. En segundo lugar, es necesario expandir el número de camas disponibles. A pesar de que el sector privado contribuya a amortiguar el déficit de demanda, la oferta conjunta de los dos sectores no sería suficiente en varias macrorregiones. La construcción de hospitales de campaña es importante, tanto en lugares donde históricamente existen lagunas asistenciales, como también en aquellos donde ya se observa una presión por parte de la demanda. El tercer mensaje se refiere a la organización por regiones de los servicios de salud que, a pesar de ser adecuada en situaciones de demanda habitual, en momentos de pandemia, este diseño implica desafíos adicionales, especialmente si la distancia que el paciente tuviera que recorrer fuera muy lejana.

COVID-19 and US Health Financing: Perils and Possibilities.

While the COVID-19 pandemic presents every nation with challenges, the United States' underfunded public health infrastructure, fragmented medical care system, and inadequate social protections impose particular impediments to mitigating and managing the outbreak. Years of inadequate funding of the nation's federal, state, and local public health agencies, together with mismanagement by the Trump administration, hampered the early response to the epidemic. Meanwhile, barriers to care faced by uninsured and underinsured individuals in the United States could deter COVID-19 care and hamper containment efforts, and lead to adverse medical and financial outcomes for infected individuals and their families, particularly those from disadvantaged groups. While the United States has a relatively generous supply of Intensive Care Unit beds and most other health care infrastructure, such medical resources are often unevenly distributed or deployed, leaving some areas ill-prepared for a severe respiratory epidemic. These deficiencies and shortfalls have stimulated a debate about policy solutions. Recent legislation, for instance, expanded coverage for testing for COVID-19 for the uninsured and underinsured, and additional reforms have been proposed. However comprehensive health care reform - for example, via national health insurance - is needed to provide full protection to American families during the COVID-19 outbreak and in its aftermath.

Urgent Peritoneal Dialysis in Patients With COVID-19 and Acute Kidney Injury: A Single-Center Experience in a Time of Crisis in the United States.

At Montefiore Medical Center in The Bronx, NY, the first case of coronavirus disease 2019 (COVID-19) was admitted on March 11, 2020. At the height of the pandemic, there were 855 patients with COVID-19 admitted on April 13, 2020. Due to high demand for dialysis and shortages of staff and supplies, we started an urgent peritoneal dialysis (PD) program. From April 1 to April 22, a total of 30 patients were started on PD. Of those 30 patients, 14 died during their hospitalization, 8 were discharged, and 8 were still hospitalized as of May 14, 2020. Although the PD program was successful in its ability to provide much-needed kidney replacement therapy when hemodialysis was not available, challenges to delivering adequate PD dosage included difficulties providing nurse training and availability of supplies. Providing adequate clearance and ultrafiltration for patients in intensive care units was especially difficult due to the high prevalence of a hypercatabolic state, volume overload, and prone positioning. PD was more easily performed in non-critically ill patients outside the intensive care unit. Despite these challenges, we demonstrate that urgent PD is a feasible alternative to hemodialysis in situations with critical resource shortages.