Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 38
Filtrar
Añadir filtros

Intervalo de año
1.
PLoS One ; 15(10): e0241027, 2020.
Artículo en Inglés | MEDLINE | ID: covidwho-883688

RESUMEN

As the number of cases of COVID-19 continues to grow, local health services are at risk of being overwhelmed with patients requiring intensive care. We develop and implement an algorithm to provide optimal re-routing strategies to either transfer patients requiring Intensive Care Units (ICU) or ventilators, constrained by feasibility of transfer. We validate our approach with realistic data from the United Kingdom and Spain. In the UK, we consider the National Health Service at the level of trusts and define a 4-regular geometric graph which indicates the four nearest neighbours of any given trust. In Spain we coarse-grain the healthcare system at the level of autonomous communities, and extract similar contact networks. Through random search optimisation we identify the best load sharing strategy, where the cost function to minimise is based on the total number of ICU units above capacity. Our framework is general and flexible allowing for additional criteria, alternative cost functions, and can be extended to other resources beyond ICU units or ventilators. Assuming a uniform ICU demand, we show that it is possible to enable access to ICU for up to 1000 additional cases in the UK in a single step of the algorithm. Under a more realistic and heterogeneous demand, our method is able to balance about 600 beds per step in the Spanish system only using local sharing, and over 1300 using countrywide sharing, potentially saving a large percentage of these lives that would otherwise not have access to ICU.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/terapia , Recursos en Salud/provisión & distribución , Modelos Teóricos , Neumonía Viral/epidemiología , Neumonía Viral/terapia , Algoritmos , Infecciones por Coronavirus/virología , Cuidados Críticos , Capacidad de Camas en Hospitales , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Pandemias , Transferencia de Pacientes , Neumonía Viral/virología , España/epidemiología , Reino Unido/epidemiología , Ventiladores Mecánicos/provisión & distribución
2.
Elife ; 92020 10 12.
Artículo en Inglés | MEDLINE | ID: covidwho-844205

RESUMEN

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.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/terapia , Necesidades y Demandas de Servicios de Salud , Pandemias , Neumonía Viral/terapia , Adulto , Anciano , Área Bajo la Curva , Índice de Masa Corporal , Comorbilidad , Infecciones por Coronavirus/epidemiología , Diabetes Mellitus/epidemiología , Femenino , Hospitalización/estadística & datos numéricos , Humanos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Unidades de Cuidados Intensivos/provisión & distribución , Masculino , Massachusetts/epidemiología , Persona de Mediana Edad , Dinámicas no Lineales , Neumonía Viral/epidemiología , Utilización de Procedimientos y Técnicas , Curva ROC , Respiración Artificial/estadística & datos numéricos , Factores de Riesgo , Ventiladores Mecánicos/provisión & distribución
3.
Lancet ; 395(10231): 1225-1228, 2020 04 11.
Artículo en Inglés | MEDLINE | ID: covidwho-830460

RESUMEN

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.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/epidemiología , Neumonía Viral/epidemiología , Adulto , Anciano , Anciano de 80 o más Años , Infecciones por Coronavirus/terapia , Femenino , Salud Global , Política de Salud/tendencias , Capacidad de Camas en Hospitales/estadística & datos numéricos , Humanos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Unidades de Cuidados Intensivos/provisión & distribución , Italia/epidemiología , Masculino , Persona de Mediana Edad , Modelos Biológicos , Pandemias , Neumonía Viral/terapia , Respiración Artificial/estadística & datos numéricos
4.
PLoS One ; 15(9): e0239249, 2020.
Artículo en Inglés | MEDLINE | ID: covidwho-788880

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/terapia , Capacidad de Camas en Hospitales/estadística & datos numéricos , Unidades de Cuidados Intensivos/provisión & distribución , Neumonía Viral/terapia , Betacoronavirus , Infecciones por Coronavirus/epidemiología , Prestación de Atención de Salud/normas , Brotes de Enfermedades , Capacidad de Camas en Hospitales/normas , Humanos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Italia/epidemiología , Pandemias , Manejo de Atención al Paciente/normas , Neumonía Viral/epidemiología
5.
Am J Kidney Dis ; 76(5): 696-709.e1, 2020 11.
Artículo en Inglés | MEDLINE | ID: covidwho-676627

RESUMEN

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.


Asunto(s)
Lesión Renal Aguda , Defensa Civil , Terapia de Reemplazo Renal Continuo/métodos , Infecciones por Coronavirus , Enfermedad Crítica , Necesidades y Demandas de Servicios de Salud/organización & administración , Unidades de Cuidados Intensivos/provisión & distribución , Pandemias , Neumonía Viral , Reserva Estratégica/métodos , Lesión Renal Aguda/etiología , Lesión Renal Aguda/terapia , Betacoronavirus , Defensa Civil/métodos , Defensa Civil/organización & administración , Infecciones por Coronavirus/complicaciones , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/terapia , Enfermedad Crítica/epidemiología , Enfermedad Crítica/terapia , Humanos , Modelos Teóricos , Neumonía Viral/complicaciones , Neumonía Viral/epidemiología , Neumonía Viral/terapia , Utilización de Procedimientos y Técnicas/estadística & datos numéricos , Medición de Riesgo/métodos , Estados Unidos/epidemiología
7.
CMAJ ; 192(44): E1347-E1356, 2020 11 02.
Artículo en Inglés | MEDLINE | ID: covidwho-740406

RESUMEN

BACKGROUND: To mitigate the effects of coronavirus disease 2019 (COVID-19), jurisdictions worldwide ramped down nonemergent surgeries, creating a global surgical backlog. We sought to estimate the size of the nonemergent surgical backlog during COVID-19 in Ontario, Canada, and the time and resources required to clear the backlog. METHODS: We used 6 Ontario or Canadian population administrative sources to obtain data covering part or all of the period between Jan. 1, 2017, and June 13, 2020, on historical volumes and operating room throughput distributions by surgery type and region, and lengths of stay in ward and intensive care unit (ICU) beds. We used time series forecasting, queuing models and probabilistic sensitivity analysis to estimate the size of the backlog and clearance time for a +10% (+1 day per week at 50% capacity) surge scenario. RESULTS: Between Mar. 15 and June 13, 2020, the estimated backlog in Ontario was 148 364 surgeries (95% prediction interval 124 508-174 589), an average weekly increase of 11 413 surgeries. Estimated backlog clearance time is 84 weeks (95% confidence interval [CI] 46-145), with an estimated weekly throughput of 717 patients (95% CI 326-1367) requiring 719 operating room hours (95% CI 431-1038), 265 ward beds (95% CI 87-678) and 9 ICU beds (95% CI 4-20) per week. INTERPRETATION: The magnitude of the surgical backlog from COVID-19 raises serious implications for the recovery phase in Ontario. Our framework for modelling surgical backlog recovery can be adapted to other jurisdictions, using local data to assist with planning.


Asunto(s)
Procedimientos Quirúrgicos Cardíacos/estadística & datos numéricos , Infecciones por Coronavirus , Neoplasias/cirugía , Trasplante de Órganos/estadística & datos numéricos , Pandemias , Neumonía Viral , Procedimientos Quirúrgicos Vasculares/estadística & datos numéricos , Betacoronavirus , Procedimientos Quirúrgicos Electivos/estadística & datos numéricos , Predicción , Capacidad de Camas en Hospitales/estadística & datos numéricos , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Tiempo de Internación/estadística & datos numéricos , Modelos Estadísticos , Ontario , Quirófanos/provisión & distribución , Pediatría/estadística & datos numéricos , Factores de Tiempo
8.
Ann Glob Health ; 86(1): 100, 2020 08 13.
Artículo en Inglés | MEDLINE | ID: covidwho-736810

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus , Accesibilidad a los Servicios de Salud/organización & administración , Unidades de Cuidados Intensivos/provisión & distribución , Pandemias , Manejo de Atención al Paciente , Neumonía Viral , Sector Privado/estadística & datos numéricos , Sector Público/estadística & datos numéricos , Ocupación de Camas/estadística & datos numéricos , Betacoronavirus , Brasil/epidemiología , Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/prevención & control , Infecciones por Coronavirus/terapia , Necesidades y Demandas de Servicios de Salud , Humanos , Control de Infecciones/organización & administración , Control de Infecciones/normas , Innovación Organizacional , Pandemias/prevención & control , Manejo de Atención al Paciente/organización & administración , Manejo de Atención al Paciente/normas , Neumonía Viral/epidemiología , Neumonía Viral/prevención & control , Neumonía Viral/terapia , Índice de Severidad de la Enfermedad
10.
Oncology (Williston Park) ; 34(8): 317-319, 2020 08 12.
Artículo en Inglés | MEDLINE | ID: covidwho-713075

RESUMEN

A 78-year-old man had a medical history of hypertension, atrial fibrillation, chronic kidney disease, and metastatic castration-resistant prostate cancer (CRPC). He had progressed to first-line therapy for CRPC with abiraterone plus androgen-deprivation therapy (ADT) and as second-line therapy he was being treated with docetaxel, with biochemical progression in his last prostate specific antigen measurement. He was admitted to the hospital on April 2020, in the middle of the coronavirus disease 2019 (COVID-19) pandemic, because of painful bone lesions and deterioration of renal function.


Asunto(s)
Anticoagulantes/uso terapéutico , Neoplasias Óseas/tratamiento farmacológico , Infecciones por Coronavirus/terapia , Cuidados Paliativos , Neumonía Viral/terapia , Neoplasias de la Próstata Resistentes a la Castración/tratamiento farmacológico , Insuficiencia Respiratoria/terapia , Anciano , Antagonistas de Andrógenos/uso terapéutico , Androstenos/uso terapéutico , Antineoplásicos/uso terapéutico , Betacoronavirus , Conservadores de la Densidad Ósea/uso terapéutico , Neoplasias Óseas/complicaciones , Neoplasias Óseas/secundario , Dolor en Cáncer/complicaciones , Dolor en Cáncer/terapia , Infecciones por Coronavirus/complicaciones , Progresión de la Enfermedad , Docetaxel/uso terapéutico , Combinación de Medicamentos , Determinación de la Elegibilidad , Heparina de Bajo-Peso-Molecular/uso terapéutico , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Lopinavir/uso terapéutico , Masculino , Terapia por Inhalación de Oxígeno , Pandemias , Neumonía Viral/complicaciones , Neoplasias de la Próstata Resistentes a la Castración/complicaciones , Neoplasias de la Próstata Resistentes a la Castración/patología , Insuficiencia Renal , Insuficiencia Respiratoria/etiología , Reacción en Cadena de la Polimerasa de Transcriptasa Inversa , Ritonavir/uso terapéutico , Índice de Severidad de la Enfermedad , Ácido Zoledrónico/uso terapéutico
11.
Eur J Epidemiol ; 35(8): 733-742, 2020 Aug.
Artículo en Inglés | MEDLINE | ID: covidwho-708706

RESUMEN

Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we dissect the predictions made by four models for the daily COVID-19 death counts between March 25 and June 5 in New York state, as well as the predictions of ICU bed utilisation made by the influential IHME model. We evaluated the accuracy of the point estimates and the accuracy of the uncertainty estimates of the model predictions. First, we compared the "ground truth" data sources on daily deaths against which these models were trained. Three different data sources were used by these models, and these had substantial differences in recorded daily death counts. Two additional data sources that we examined also provided different death counts per day. For accuracy of prediction, all models fared very poorly. Only 10.2% of the predictions fell within 10% of their training ground truth, irrespective of distance into the future. For accurate assessment of uncertainty, only one model matched relatively well the nominal 95% coverage, but that model did not start predictions until April 16, thus had no impact on early, major decisions. For ICU bed utilisation, the IHME model was highly inaccurate; the point estimates only started to match ground truth after the pandemic wave had started to wane. We conclude that trustworthy models require trustworthy input data to be trained upon. Moreover, models need to be subjected to prespecified real time performance tests, before their results are provided to policy makers and public health officials.


Asunto(s)
Infecciones por Coronavirus/mortalidad , Predicción/métodos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Pandemias/prevención & control , Neumonía Viral/mortalidad , Ocupación de Camas , Betacoronavirus , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Modelos Estadísticos , Mortalidad/tendencias , New York/epidemiología , Salud Pública
14.
J Biol Dyn ; 14(1): 621-632, 2020 12.
Artículo en Inglés | MEDLINE | ID: covidwho-680239

RESUMEN

We model the extent to which age-targeted protective sequestration can be used to reduce ICU admissions caused by novel coronavirus COVID-19. Using demographic data from New Zealand, we demonstrate that lowering the age threshold to 50 years of age reduces ICU admissions drastically and show that for sufficiently strict isolation protocols, sequestering one-third of the countries population for a total of 8 months is sufficient to avoid overwhelming ICU capacity throughout the entire course of the epidemic. Similar results are expected to hold for other countries, though some minor adaption will be required based on local age demographics and hospital facilities.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/epidemiología , Modelos Biológicos , Pandemias , Neumonía Viral/epidemiología , Cuarentena/métodos , Adolescente , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Niño , Preescolar , Control de Enfermedades Transmisibles/métodos , Simulación por Computador , Infecciones por Coronavirus/prevención & control , Infecciones por Coronavirus/transmisión , Cuidados Críticos , Femenino , Hospitalización , Humanos , Lactante , Recién Nacido , Unidades de Cuidados Intensivos/provisión & distribución , Masculino , Persona de Mediana Edad , Nueva Zelanda/epidemiología , Pandemias/prevención & control , Aislamiento de Pacientes/métodos , Neumonía Viral/prevención & control , Neumonía Viral/transmisión , Factores de Riesgo , Adulto Joven
15.
PLoS One ; 15(7): e0236308, 2020.
Artículo en Inglés | MEDLINE | ID: covidwho-650507

RESUMEN

INTRODUCTION: The COVID-19 pandemic will test the capacity of health systems worldwide and especially so in low- and middle-income countries. The objective of this study was to assess the surge capacity of the Kenyan of the Kenyan health system in terms of general hospital and ICU beds in the face of the COVID-19 pandemic. METHODS: We assumed that 2% of the Kenyan population get symptomatic infection by SARS-Cov-2 based on modelled estimates for Kenya and determined the health system surge capacity for COVID-19 under three transmission curve scenarios, 6, 12, and 18 months. We estimated four measures of hospital surge capacity namely: 1) hospital bed surge capacity 2) ICU bed surge capacity 3) Hospital bed tipping point, and 5) ICU bed tipping point. We computed this nationally and for all the 47 county governments. RESULTS: The capacity of Kenyan hospitals to absorb increases in caseload due to COVID-19 is constrained by the availability of oxygen, with only 58% of hospital beds in hospitals with oxygen supply. There is substantial variation in hospital bed surge capacity across counties. For example, under the 6 months transmission scenario, the percentage of available general hospital beds that would be taken up by COVID-19 cases varied from 12% Tharaka Nithi county, to 145% in Trans Nzoia county. Kenya faces substantial gaps in ICU beds and ventilator capacity. Only 22 out of the 47 counties have at least 1 ICU unit. Kenya will need an additional 1,511 ICU beds and 1,609 ventilators (6 months transmission curve) to 374 ICU beds and 472 ventilators (18 months transmission curve) to absorb caseloads due to COVID-19. CONCLUSION: Significant gaps exist in Kenya's capacity for hospitals to accommodate a potential surge in caseload due to COVID-19. Alongside efforts to slow and supress the transmission of the infection, the Kenyan government will need to implement adaptive measures and additional investments to expand the hospital surge capacity for COVID-19. Additional investments will however need to be strategically prioritized to focus on strengthening essential services first, such as oxygen availability before higher cost investments such as ICU beds and ventilators.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Capacidad de Camas en Hospitales , Neumonía Viral/epidemiología , Capacidad de Reacción , Ventiladores Mecánicos/provisión & distribución , Betacoronavirus , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Kenia/epidemiología , Pandemias
16.
Acta Neurochir (Wien) ; 162(9): 2221-2233, 2020 09.
Artículo en Inglés | MEDLINE | ID: covidwho-635738

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Necesidades y Demandas de Servicios de Salud/estadística & datos numéricos , Unidades de Cuidados Intensivos/provisión & distribución , Procedimientos Neuroquirúrgicos/estadística & datos numéricos , Neumonía Viral/epidemiología , Servicio de Cirugía en Hospital/provisión & distribución , Europa (Continente) , Recursos en Salud/provisión & distribución , Humanos , Pandemias , Encuestas y Cuestionarios
17.
J Infect Public Health ; 13(9): 1237-1239, 2020 Sep.
Artículo en Inglés | MEDLINE | ID: covidwho-598896

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Prestación de Atención de Salud/organización & administración , Servicio de Urgencia en Hospital , Recursos en Salud/provisión & distribución , Pandemias , Neumonía Viral/epidemiología , Betacoronavirus , Infecciones por Coronavirus/terapia , Servicio de Urgencia en Hospital/organización & administración , Francia , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Neumonía Viral/terapia , Triaje
18.
Cien Saude Colet ; 25(suppl 1): 2461-2468, 2020 Jun.
Artículo en Portugués, Inglés | MEDLINE | ID: covidwho-594941

RESUMEN

The geographical distribution of COVID-19 through Geographic Information Systems resources is hardly explored. We aimed to analyze the distribution of COVID-19 cases and the exclusive intensive care beds in the state of Ceará, Brazil. This is an ecological study with the geographic distribution of the case detection coefficient in 184 municipalities. Maps of crude and estimated values (global and local Bayesian method) were developed, calculating the Moran index and using BoxMap and MoranMap. Intensive care beds were distributed through geolocalized points. In total, 3,000 cases and 459 beds were studied. The highest rates were found in the capital Fortaleza, the Metropolitan Region (MR), and the south of this region. A positive spatial autocorrelation has been identified in the local Bayesian rate (I = 0.66). The distribution of beds superimposed on the BoxMap shows clusters with a High-High pattern of number of beds (capital, MR, northwestern part). However, a similar pattern is found in the far east or transition areas with insufficient beds. The MoranMap shows clusters statistically significant in the state. COVID-19 interiorization in Ceará requires contingency measures geared to the distribution of specific intensive care beds for COVID-19 cases in order to meet the demand.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/epidemiología , Mapeo Geográfico , Capacidad de Camas en Hospitales/estadística & datos numéricos , Unidades de Cuidados Intensivos/provisión & distribución , Pandemias , Neumonía Viral/epidemiología , Teorema de Bayes , Brasil/epidemiología , Infecciones por Coronavirus/transmisión , Sistemas de Información Geográfica , Humanos , Neumonía Viral/transmisión
19.
Am J Kidney Dis ; 76(3): 401-406, 2020 09.
Artículo en Inglés | MEDLINE | ID: covidwho-593700

RESUMEN

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.


Asunto(s)
Lesión Renal Aguda/terapia , Betacoronavirus , Infecciones por Coronavirus/terapia , Necesidades y Demandas de Servicios de Salud , Diálisis Peritoneal/métodos , Neumonía Viral/terapia , Lesión Renal Aguda/epidemiología , Infecciones por Coronavirus/epidemiología , Soluciones para Diálisis/provisión & distribución , Necesidades y Demandas de Servicios de Salud/tendencias , Humanos , Unidades de Cuidados Intensivos/provisión & distribución , Unidades de Cuidados Intensivos/tendencias , Pandemias , Diálisis Peritoneal/tendencias , Neumonía Viral/epidemiología , Estados Unidos/epidemiología
20.
Int J Health Serv ; 50(4): 396-407, 2020 10.
Artículo en Inglés | MEDLINE | ID: covidwho-591800

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Gastos en Salud/estadística & datos numéricos , Neumonía Viral/epidemiología , Administración en Salud Pública/economía , Betacoronavirus , Técnicas de Laboratorio Clínico , Control de Enfermedades Transmisibles/organización & administración , Infecciones por Coronavirus/diagnóstico , Reforma de la Atención de Salud/organización & administración , Humanos , Unidades de Cuidados Intensivos/economía , Unidades de Cuidados Intensivos/provisión & distribución , Pacientes no Asegurados , Pandemias , Estados Unidos/epidemiología
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA