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1.
Clin Microbiol Infect ; 27(1): 118-124, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-32979575

RESUMO

OBJECTIVES: The case fatality rate (CFR) of coronavirus disease 2019 (COVID-19) varies significantly between countries. We aimed to describe the associations between health indicators and the national CFRs of COVID-19. METHODS: We identified for each country health indicators potentially associated with the national CFRs of COVID-19. We extracted data for 18 variables from international administrative data sources for 34 member countries of the Organization for Economic Cooperation and Development (OECD). We excluded the collinear variables and examined the 16 variables in multivariable analysis. A dynamic web-based model was developed to analyse and display the associations for the CFRs of COVID-19. We followed the Guideline for Accurate and Transparent Health Estimates Reporting (GATHER). RESULTS: In multivariable analysis, the variables significantly associated with the increased CFRs were percentage of obesity in ages >18 years (ß = 3.26; 95%CI = 1.20, 5.33; p 0.003), tuberculosis incidence (ß = 3.15; 95%CI = 1.09, 5.22; p 0.004), duration (days) since first death due to COVID-19 (ß = 2.89; 95%CI = 0.83, 4.96; p 0.008), and median age (ß = 2.83; 95%CI = 0.76, 4.89; p 0.009). The COVID-19 test rate (ß = -3.54; 95%CI = -5.60, -1.47; p 0.002), hospital bed density (ß = -2.47; 95%CI = -4.54, -0.41; p 0.021), and rural population ratio (ß = -2.19; 95%CI = -4.25, -0.13; p 0.039) decreased the CFR. CONCLUSIONS: The pandemic hits population-dense cities. Available hospital beds should be increased. Test capacity should be increased to enable more effective diagnostic tests. Older patients and patients with obesity and their caregivers should be warned about a potentially increased risk.


Assuntos
/epidemiologia , Obesidade/epidemiologia , Obesidade/mortalidade , Tuberculose Pulmonar/epidemiologia , Tuberculose Pulmonar/mortalidade , Adulto , Fatores Etários , Idoso , América/epidemiologia , Austrália/epidemiologia , /patologia , Comorbidade , Europa (Continente)/epidemiologia , Feminino , Número de Leitos em Hospital/estatística & dados numéricos , Humanos , Japão/epidemiologia , Masculino , Pessoa de Meia-Idade , Obesidade/diagnóstico , Obesidade/patologia , Densidade Demográfica , População Rural , Índice de Gravidade de Doença , Fatores de Tempo , Tuberculose Pulmonar/diagnóstico , Tuberculose Pulmonar/patologia , População Urbana
2.
Eur J Hosp Pharm ; 28(1): 10-15, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33277234

RESUMO

INTRODUCTION: Hospital admissions from COVID-19 initially increased rapidly within the UK. National Health Service (NHS) field hospitals are part of a capacity building response built at great scale and speed to respond to the anticipated increased demand the NHS faces during this time. NHS Nightingale Hospital Birmingham (NHB) is modelled to treat mild to moderate (non-critical care) COVID-19 disease, to provide step-down capacity for patients in recovery, or for palliating patients in the dying phase of their disease in the Midlands. Opportunities and challenges presented for optimal medicines management (MM) during the development of the NHB are investigated, and a framework developed to support future NHS field hospitals of this model. METHODS: A team, comprised of an associate medical director, trust chief pharmacist and senior pharmacists iteratively developed a framework to convert the large non-hospital setting into a functioning NHS field hospital with standardised MM processes adjusted appropriately to cope with operational constraints in the pandemic situation. NHB has, because of its repurposing, both challenges and advantages affecting MM that influence development of the framework. Throughout implementation, a 7-week period between announcement and opening, there was continuous evaluation, external stakeholder validation and peer review. RESULTS: The PESTLE model, a mechanism of analysis to identify elements of a project environment (Political, Environmental, Social, Technological, Legal and Economic), was applied to identify influencing factors and support detailed project planning. Compliance with medicines legislation was at the forefront of all MM process development for the NHB field hospital. Internal factors were identified by the core MM team, resulting in a workforce, education & training and clinical pharmacy MM plan. DISCUSSION: MM processes are extensive and integral to NHS field hospitals. The presented framework of influencing factors may support future NHS field hospital development. It is pertinent to have a broad team working approach to any large-scale project such as outlined here, and suggest the identified factors be used as a core framework for development of any future MM processes in NHS field hospitals.


Assuntos
Administração Hospitalar/tendências , Conduta do Tratamento Medicamentoso/organização & administração , Unidades Móveis de Saúde/organização & administração , Pandemias , Medicina Estatal/organização & administração , Planejamento de Instituições de Saúde , Número de Leitos em Hospital/estatística & dados numéricos , Humanos , Sistemas de Medicação no Hospital , Conduta do Tratamento Medicamentoso/legislação & jurisprudência , Modelos Organizacionais , Política Organizacional , Farmacêuticos , Serviço de Farmácia Hospitalar , Medicina Estatal/legislação & jurisprudência , Reino Unido , Recursos Humanos
3.
Bull World Health Organ ; 98(12): 830-841D, 2020 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-33293743

RESUMO

Objective: To design models of the spread of coronavirus disease-2019 (COVID-19) in Wuhan and the effect of Fangcang shelter hospitals (rapidly-built temporary hospitals) on the control of the epidemic. Methods: We used data on daily reported confirmed cases of COVID-19, recovered cases and deaths from the official website of the Wuhan Municipal Health Commission to build compartmental models for three phases of the COVID-19 epidemic. We incorporated the hospital-bed capacity of both designated and Fangcang shelter hospitals. We used the models to assess the success of the strategy adopted in Wuhan to control the COVID-19 epidemic. Findings: Based on the 13 348 Fangcang shelter hospitals beds used in practice, our models show that if the Fangcang shelter hospitals had been opened on 6 February (a day after their actual opening), the total number of COVID-19 cases would have reached 7 413 798 (instead of 50 844) with 1 396 017 deaths (instead of 5003), and the epidemic would have lasted for 179 days (instead of 71). Conclusion: While the designated hospitals saved lives of patients with severe COVID-19, it was the increased hospital-bed capacity of the large number of Fangcang shelter hospitals that helped slow and eventually stop the COVID-19 epidemic in Wuhan. Given the current global pandemic of COVID-19, our study suggests that increasing hospital-bed capacity, especially through temporary hospitals such as Fangcang shelter hospitals, to isolate groups of people with mild symptoms within an affected region could help curb and eventually stop COVID-19 outbreaks in communities where effective household isolation is not possible.


Assuntos
/epidemiologia , Número de Leitos em Hospital/estatística & dados numéricos , Unidades Móveis de Saúde/organização & administração , China/epidemiologia , Humanos , Cadeias de Markov , Modelos Estatísticos , Pandemias
4.
J Prev Med Public Health ; 53(6): 387-396, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33296578

RESUMO

OBJECTIVES: The lack of advance planning in a public health emergency can lead to wasted resources and inadvertent loss of lives. This study is aimed at forecasting the needs for healthcare resources following the expansion of the coronavirus disease 2019 (COVID-19) outbreak in the Republic of Kazakhstan, focusing on hospital beds, equipment, and the professional workforce in light of the developing epidemiological situation and the data on resources currently available. METHODS: We constructed a forecast model of the epidemiological scenario via the classic susceptible-exposed-infected-removed (SEIR) approach. The World Health Organization's COVID-19 Essential Supplies Forecasting Tool was used to evaluate the healthcare resources needed for the next 12 weeks. RESULTS: Over the forecast period, there will be 104 713.7 hospital admissions due to severe disease and 34 904.5 hospital admissions due to critical disease. This will require 47 247.7 beds for severe disease and 1929.9 beds for critical disease at the peak of the COVID-19 outbreak. There will also be high needs for all categories of healthcare workers and for both diagnostic and treatment equipment. Thus, Republic of Kazakhstan faces the need for a rapid increase in available healthcare resources and/or for finding ways to redistribute resources effectively. CONCLUSIONS: Republic of Kazakhstan will be able to reduce the rates of infections and deaths among its population by developing and following a consistent strategy targeting COVID-19 in a number of inter-related directions.


Assuntos
/epidemiologia , Controle de Doenças Transmissíveis/tendências , Surtos de Doenças/prevenção & controle , Pessoal de Saúde/tendências , Pandemias/prevenção & controle , /terapia , Número de Leitos em Hospital/estatística & dados numéricos , Humanos , Unidades de Terapia Intensiva/tendências , Cazaquistão/epidemiologia , Aceitação pelo Paciente de Cuidados de Saúde
5.
BMC Health Serv Res ; 20(1): 1119, 2020 Dec 03.
Artigo em Inglês | MEDLINE | ID: mdl-33272278

RESUMO

BACKGROUND: To increase bed capacity and resources, hospitals have postponed elective surgeries, although the financial impact of this decision is unknown. We sought to report elective surgical case distribution, associated gross hospital revenue and regional hospital and intensive care unit (ICU) bed capacity as elective surgical cases are cancelled and then resumed under simulated trends of COVID-19 incidence. METHODS: A retrospective, cohort analysis was performed using insurance claims from 161 million enrollees from the MarketScan database from January 1, 2008 to December 31, 2017. COVID-19 cases were calculated using Institute for Health Metrics and Evaluation models. Centers for Disease Control (CDC) reports on the number of hospitalized and intensive care patients by age estimated the number of cases seen in the ICU, the reduction in elective surgeries and the financial impact of this from historic claims data, using a denominator of all inpatient revenue and outpatient surgeries. RESULTS: Assuming 5% infection prevalence, cancelling all elective procedures decreases ICU overcapacity from 160 to 130%, but these elective surgical cases contribute 78% (IQR 74, 80) (1.1 trillion (T) US dollars) to inpatient hospital plus outpatient surgical gross revenue per year. Musculoskeletal, circulatory and digestive category elective surgical cases compose 33% ($447B) of total revenue. CONCLUSIONS: Procedures involving the musculoskeletal, cardiovascular and digestive system account for the largest loss of hospital gross revenue when elective surgery is postponed. As hospital bed capacity increases following the COVID-19 pandemic, restoring volume of these elective cases will help maintain revenue. In these estimates, adopting universal masking would help to avoid overcapacity in all states.


Assuntos
/epidemiologia , Procedimentos Cirúrgicos Eletivos/economia , Número de Leitos em Hospital/estatística & dados numéricos , Pandemias , Economia Hospitalar , Procedimentos Cirúrgicos Eletivos/estatística & dados numéricos , Humanos , Unidades de Terapia Intensiva , Estudos Retrospectivos , Estados Unidos/epidemiologia
6.
Artigo em Inglês | MEDLINE | ID: mdl-33218133

RESUMO

The global outbreak of COVID-19 has caused worrying concern amongst the public and health authorities. The first and foremost problem that many countries face during the outbreak is a shortage of medical resources. In order to investigate the impact of a shortage of hospital beds on the COVID-19 outbreak, we formulated a piecewise smooth model for describing the limitation of hospital beds. We parameterized the model while using data on the cumulative numbers of confirmed cases, recovered cases, and deaths in Wuhan city from 10 January to 12 April 2020. The results showed that, even with strong prevention and control measures in Wuhan, slowing down the supply rate, reducing the maximum capacity, and delaying the supply time of hospital beds all aggravated the outbreak severity by magnifying the cumulative numbers of confirmed cases and deaths, lengthening the end time of the pandemic, enlarging the value of the effective reproduction number during the outbreak, and postponing the time when the threshold value was reduced to 1. Our results demonstrated that establishment of the Huoshenshan, Leishenshan, and Fangcang shelter hospitals avoided 22,786 people from being infected and saved 6524 lives. Furthermore, the intervention of supplying hospital beds avoided infections in 362,360 people and saved the lives of 274,591 persons. This confirmed that the quick establishment of the Huoshenshan, Leishenshan Hospitals, and Fangcang shelter hospitals, and the designation of other hospitals for COVID-19 patients played important roles in containing the outbreak in Wuhan.


Assuntos
Leitos/provisão & distribução , Infecções por Coronavirus/epidemiologia , Número de Leitos em Hospital/estatística & dados numéricos , Pneumonia Viral/epidemiologia , Betacoronavirus , China/epidemiologia , Humanos , Pandemias
7.
PLoS One ; 15(10): e0240645, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33052968

RESUMO

INTRODUCTION: Because of the COVID-19 pandemic, intensive care units (ICU) can be overwhelmed by the number of hypoxemic patients. MATERIAL AND METHODS: This single centre retrospective observational cohort study took place in a French hospital where the number of patients exceeded the ICU capacity despite an increase from 18 to 32 beds. Because of this, 59 (37%) of the 159 patients requiring ICU care were referred to other hospitals. From 27th March to 23rd April, consecutive patients who had respiratory failure or were unable to maintain an SpO2 > 90%, despite receiving 10-15 l/min of oxygen with a non-rebreather mask, were treated by continuous positive airway pressure (CPAP) unless the ICU physician judged that immediate intubation was indicated. We describe the characteristics, clinical course, and outcomes of these patients. The main outcome under study was CPAP discontinuation. RESULTS: CPAP was initiated in 49 patients and performed out of ICU in 41 (84%). Median age was 65 years (IQR = 54-71) and 36 (73%) were men. Median respiratory rate before CPAP was 36 (30-40) and median SpO2 was 92% (90-95) under 10 to 15 L/min oxygen flow. Median duration of CPAP was 3 days (IQR = 1-5). Reasons for discontinuation of CPAP were: intubation in 25 (51%), improvement in 16 (33%), poor tolerance in 6 (12%) and death in 2 (4%) patients. A decision not to intubate had been taken for 8 patients, including the 2 who died while on CPAP. Two patients underwent less than one hour CPAP for poor tolerance. In the end, 15 (38%) out of 39 evaluable patients recovered with only CPAP whereas 24 (62%) were intubated. CONCLUSIONS: CPAP is feasible in a non-ICU environment in the context of massive influx of patients. In our cohort up to 1/3 of the patients presenting with acute respiratory failure recovered without intubation.


Assuntos
Pressão Positiva Contínua nas Vias Aéreas/métodos , Infecções por Coronavirus/terapia , Pneumonia Viral/terapia , Idoso , Pressão Positiva Contínua nas Vias Aéreas/economia , Pressão Positiva Contínua nas Vias Aéreas/instrumentação , Infecções por Coronavirus/economia , Infecções por Coronavirus/epidemiologia , Custos e Análise de Custo , Feminino , França , Número de Leitos em Hospital/estatística & dados numéricos , Humanos , Unidades de Terapia Intensiva/estatística & dados numéricos , Masculino , Pessoa de Meia-Idade , Pandemias/economia , Admissão do Paciente/estatística & dados numéricos , Pneumonia Viral/economia , Pneumonia Viral/epidemiologia
8.
Anaesth Crit Care Pain Med ; 39(6): 709-715, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-33031979

RESUMO

BACKGROUND: Whereas 5415 Intensive Care Unit (ICU) beds were initially available, 7148 COVID-19 patients were hospitalised in the ICU at the peak of the outbreak. The present study reports how the French Health Care system created temporary ICU beds to avoid being overwhelmed. METHODS: All French ICUs were contacted for answering a questionnaire focusing on the available beds and health care providers before and during the outbreak. RESULTS: Among 336 institutions with ICUs before the outbreak, 315 (94%) participated, covering 5054/5531 (91%) ICU beds. During the outbreak, 4806 new ICU beds (+95% increase) were created from Acute Care Unit (ACU, 2283), Post Anaesthetic Care Unit and Operating Theatre (PACU & OT, 1522), other units (374) or real build-up of new ICU beds (627), respectively. At the peak of the outbreak, 9860, 1982 and 3089 ICU, ACU and PACU beds were made available. Before the outbreak, 3548 physicians (2224 critical care anaesthesiologists, 898 intensivists and 275 from other specialties, 151 paediatrics), 1785 residents, 11,023 nurses and 6763 nursing auxiliaries worked in established ICUs. During the outbreak, 2524 physicians, 715 residents, 7722 nurses and 3043 nursing auxiliaries supplemented the usual staff in all ICUs. A total number of 3212 new ventilators were added to the 5997 initially available in ICU. CONCLUSION: During the COVID-19 outbreak, the French Health Care system created 4806 ICU beds (+95% increase from baseline), essentially by transforming beds from ACUs and PACUs. Collaboration between intensivists, critical care anaesthesiologists, emergency physicians as well as the mobilisation of nursing staff were primordial in this context.


Assuntos
/epidemiologia , Número de Leitos em Hospital/estatística & dados numéricos , Unidades de Terapia Intensiva/estatística & dados numéricos , Programas Nacionais de Saúde , Pandemias , Conversão de Leitos/estatística & dados numéricos , França/epidemiologia , Pesquisas sobre Serviços de Saúde/estatística & dados numéricos , Hospitalização/estatística & dados numéricos , Humanos , Admissão e Escalonamento de Pessoal/estatística & dados numéricos , Recursos Humanos em Hospital/provisão & distribução , Estudos Retrospectivos , Ventiladores Mecânicos/provisão & distribução
9.
Medwave ; 20(9): e8039, 2020 Oct 05.
Artigo em Espanhol | MEDLINE | ID: mdl-33031358

RESUMO

Introduction: SARS CoV-2 pandemic is pressing hard on the responsiveness of health systems worldwide, notably concerning the massive surge in demand for intensive care hospital beds. Aim: This study proposes a methodology to estimate the saturation moment of hospital intensive care beds (critical care beds) and determine the number of units required to compensate for this saturation. Methods: A total of 22,016 patients with diagnostic confirmation for COVID-19 caused by SARS-CoV-2 were analyzed between March 4 and May 5, 2020, nationwide. Based on information from the Chilean Ministry of Health and ministerial announcements in the media, the overall availability of critical care beds was estimated at 1,900 to 2,000. The Gompertz function was used to estimate the expected number of COVID-19 patients and to assess their exposure to the available supply of intensive care beds in various possible scenarios, taking into account the supply of total critical care beds, the average occupational index, and the demand for COVID-19 patients who would require an intensive care bed. Results: A 100% occupancy of critical care beds could be reached between May 11 and May 27. This condition could be extended for around 48 days, depending on how the expected over-demand is managed. Conclusion: A simple, easily interpretable, and applicable to all levels (nationwide, regionwide, municipalities, and hospitals) model is offered as a contribution to managing the expected demand for the coming weeks and helping reduce the adverse effects of the COVID-19 pandemic.


Assuntos
Infecções por Coronavirus/epidemiologia , Número de Leitos em Hospital/estatística & dados numéricos , Unidades de Terapia Intensiva/provisão & distribução , Modelos Estatísticos , Pneumonia Viral/epidemiologia , Chile/epidemiologia , Humanos , Pandemias
10.
Medwave ; 20(9)30-10-2020.
Artigo em Espanhol | LILACS | ID: biblio-1141137

RESUMO

Introducción La pandemia por SARS CoV-2 está presionando fuertemente la capacidad de respuesta de los sistemas de salud en todo el mundo, siendo uno de los aspectos más importantes el aumento masivo de pacientes que requerirán utilizar camas hospitalarias de cuidados intensivos. Objetivo Este estudio propone una metodología para estimar el momento de saturación de las camas de cuidados intensivos hospitalarios (camas críticas) y determinar el número de unidades requeridas para compensar dicha saturación. Método Se analizaron 22 016 pacientes con confirmación diagnóstica para COVID-19 provocada por SARS-CoV-2, entre el 4 de marzo y el 5 de mayo de 2020 a nivel nacional. Sobre la base de información del Ministerio de Salud de Chile y a anuncios ministeriales en medios de prensa, se estimó una disponibilidad total actual de 1900 a 2200 camas críticas totales. Se utilizó la función de Gompertz para estimar el número esperado de pacientes COVID-19 y evaluar su exposición a la oferta disponible de camas de cuidados intensivos en varios escenarios posibles. Para ello se tomó en cuenta la oferta de camas críticas totales, el índice ocupacional promedio, y la demanda de pacientes COVID-19 que requerirán cama de cuidados intensivos. Resultados Considerando diferentes escenarios, entre el 11 y el 27 de mayo podría ser alcanzado el 100% de ocupación de camas críticas totales. Esta condición podría extenderse por unos 48 días dependiendo como se maneje la sobredemanda esperada. Conclusión Se puede establecer una ventana de operaciones relativamente estrecha, de 4 a 8 semanas, para mitigar la inminente saturación de camas críticas hospitalarias, producto de la demanda de pacientes COVID-19.


Introduction SARS CoV-2 pandemic is pressing hard on the responsiveness of health systems worldwide, notably concerning the massive surge in demand for intensive care hospital beds. Aim This study proposes a methodology to estimate the saturation moment of hospital intensive care beds (critical care beds) and determine the number of units required to compensate for this saturation. Methods A total of 22,016 patients with diagnostic confirmation for COVID-19 caused by SARS-CoV-2 were analyzed between March 4 and May 5, 2020, nationwide. Based on information from the Chilean Ministry of Health and ministerial announcements in the media, the overall availability of critical care beds was estimated at 1,900 to 2,000. The Gompertz function was used to estimate the expected number of COVID-19 patients and to assess their exposure to the available supply of intensive care beds in various possible scenarios, taking into account the supply of total critical care beds, the average occupational index, and the demand for COVID-19 patients who would require an intensive care bed. Results A 100% occupancy of critical care beds could be reached between May 11 and May 27. This condition could be extended for around 48 days, depending on how the expected over-demand is managed. Conclusion A simple, easily interpretable, and applicable to all levels (nationwide, regionwide, municipalities, and hospitals) model is offered as a contribution to managing the expected demand for the coming weeks and helping reduce the adverse effects of the COVID-19 pandemic.


Assuntos
Humanos , Pneumonia Viral/epidemiologia , Modelos Estatísticos , Infecções por Coronavirus/epidemiologia , Número de Leitos em Hospital/estatística & dados numéricos , Unidades de Terapia Intensiva/provisão & distribução , Chile/epidemiologia , Pandemias
11.
Intensive Care Med ; 46(11): 2026-2034, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32886208

RESUMO

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.


Assuntos
Betacoronavirus , Infecções por Coronavirus/terapia , Cuidados Críticos/estatística & dados numéricos , Acesso aos Serviços de Saúde , Número de Leitos em Hospital/estatística & dados numéricos , Pneumonia Viral/terapia , Europa (Continente)/epidemiologia , Humanos , Pandemias , Análise Espacial
12.
CMAJ ; 192(44): E1347-E1356, 2020 11 02.
Artigo em Inglês | MEDLINE | ID: mdl-32873541

RESUMO

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.


Assuntos
Procedimentos Cirúrgicos Cardíacos/estatística & dados numéricos , Infecções por Coronavirus , Neoplasias/cirurgia , Transplante de Órgãos/estatística & dados numéricos , Pandemias , Pneumonia Viral , Procedimentos Cirúrgicos Vasculares/estatística & dados numéricos , Betacoronavirus , Procedimentos Cirúrgicos Eletivos/estatística & dados numéricos , Previsões , Número de Leitos em Hospital/estatística & dados numéricos , Humanos , Unidades de Terapia Intensiva/provisão & distribução , Tempo de Internação/estatística & dados numéricos , Modelos Estatísticos , Ontário , Salas Cirúrgicas/provisão & distribução , Pediatria/estatística & dados numéricos , Fatores de Tempo
13.
PLoS One ; 15(9): e0239249, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32960908

RESUMO

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.


Assuntos
Infecções por Coronavirus/terapia , Número de Leitos em Hospital/estatística & dados numéricos , Unidades de Terapia Intensiva/provisão & distribução , Pneumonia Viral/terapia , Betacoronavirus , Infecções por Coronavirus/epidemiologia , Assistência à Saúde/normas , Surtos de Doenças , Número de Leitos em Hospital/normas , Humanos , Unidades de Terapia Intensiva/estatística & dados numéricos , Itália/epidemiologia , Pandemias , Administração dos Cuidados ao Paciente/normas , Pneumonia Viral/epidemiologia
14.
Epidemiol Serv Saude ; 29(4): e2020391, 2020.
Artigo em Português, Inglês | MEDLINE | ID: mdl-32997068

RESUMO

In view of the need to manage and forecast the number of Intensive Care Unit (ICU) beds for critically ill COVID-19 patients, the Forecast UTI open access application was developed to enable hospital indicator monitoring based on past health data and the temporal dynamics of the Coronavirus epidemic. Forecast UTI also enables short-term forecasts of the number of beds occupied daily by COVID-19 patients and possible care scenarios to be established. This article presents the functions, mode of access and examples of uses of Forecast UTI, a computational tool intended to assist managers of public and private hospitals within the Brazilian National Health System by supporting quick, strategic and efficient decision-making.


Assuntos
Ocupação de Leitos/estatística & dados numéricos , Betacoronavirus , Infecções por Coronavirus/epidemiologia , Número de Leitos em Hospital/estatística & dados numéricos , Unidades de Terapia Intensiva/estatística & dados numéricos , Pneumonia Viral/epidemiologia , Software , Leitos/provisão & distribução , Brasil/epidemiologia , Tomada de Decisões , Previsões , Humanos , Pandemias , Design de Software
15.
Int J Health Geogr ; 19(1): 36, 2020 09 14.
Artigo em Inglês | MEDLINE | ID: mdl-32928236

RESUMO

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing the coronavirus disease 2019 (COVID-19) pandemic, has infected millions of people and caused hundreds of thousands of deaths. While COVID-19 has overwhelmed healthcare resources (e.g., healthcare personnel, testing resources, hospital beds, and ventilators) in a number of countries, limited research has been conducted to understand spatial accessibility of such resources. This study fills this gap by rapidly measuring the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. METHOD: The rapid measurement is achieved by resolving computational intensity of an enhanced two-step floating catchment area (E2SFCA) method through a parallel computing strategy based on cyberGIS (cyber geographic information science and systems). The E2SFCA has two major steps. First, it calculates a bed-to-population ratio for each hospital location. Second, it sums these ratios for residential locations where hospital locations overlap. RESULTS: The comparison of the spatial accessibility measures for COVID-19 patients to those of population at risk identifies which geographic areas need additional healthcare resources to improve access. The results also help delineate the areas that may face a COVID-19-induced shortage of healthcare resources. The Chicagoland, particularly the southern Chicago, shows an additional need for resources. This study also identified vulnerable population residing in the areas with low spatial accessibility in Chicago. CONCLUSION: Rapidly measuring spatial accessibility of healthcare resources provides an improved understanding of how well the healthcare infrastructure is equipped to save people's lives during the COVID-19 pandemic. The findings are relevant for policymakers and public health practitioners to allocate existing healthcare resources or distribute new resources for maximum access to health services.


Assuntos
Área Programática de Saúde/estatística & dados numéricos , Infecções por Coronavirus/epidemiologia , Recursos em Saúde/estatística & dados numéricos , Pneumonia Viral/epidemiologia , Betacoronavirus , Acesso aos Serviços de Saúde/organização & administração , Número de Leitos em Hospital/estatística & dados numéricos , Humanos , Illinois , Unidades de Terapia Intensiva/estatística & dados numéricos , Pandemias , Fatores Socioeconômicos , Análise Espacial , Ventiladores Mecânicos/provisão & distribução
16.
Emerg Infect Dis ; 26(12): 2844-2853, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-32985971

RESUMO

The ability of health systems to cope with coronavirus disease (COVID-19) cases is of major concern. In preparation, we used clinical pathway models to estimate healthcare requirements for COVID-19 patients in the context of broader public health measures in Australia. An age- and risk-stratified transmission model of COVID-19 demonstrated that an unmitigated epidemic would dramatically exceed the capacity of the health system of Australia over a prolonged period. Case isolation and contact quarantine alone are insufficient to constrain healthcare needs within feasible levels of expansion of health sector capacity. Overlaid social restrictions must be applied over the course of the epidemic to ensure systems do not become overwhelmed and essential health sector functions, including care of COVID-19 patients, can be maintained. Attention to the full pathway of clinical care is needed, along with ongoing strengthening of capacity.


Assuntos
/transmissão , Número de Leitos em Hospital/estatística & dados numéricos , Pandemias/prevenção & controle , Capacidade de Resposta ante Emergências/organização & administração , Austrália/epidemiologia , Busca de Comunicante , Procedimentos Clínicos/normas , Humanos , Unidades de Terapia Intensiva/estatística & dados numéricos , Saúde Pública , Quarentena/métodos
17.
s.l; Organización Panamericana de la Salud; sept. 8, 2020. 20 p.
Não convencional em Espanhol | LILACS | ID: biblio-1119008

RESUMO

A la fecha, se reportan 24.042 pacientes (24,8%) en aislamiento domiciliario, 1.254 pacientes (1,3%) se encuentran hospitalizados (1.105 en sala general y 149 en Unidades de Cuidado Intensivo -UCI). Se informan 69.661 casos (71,8%) como recuperados.


Assuntos
Humanos , Pneumonia Viral/epidemiologia , Infecções por Coronavirus/epidemiologia , Pandemias/estatística & dados numéricos , Betacoronavirus , Número de Leitos em Hospital/estatística & dados numéricos , Unidades de Terapia Intensiva/estatística & dados numéricos , Panamá/epidemiologia
18.
Med Care ; 58(11): 1022-1029, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32925473

RESUMO

OBJECTIVE: The objective of this study was to examine variation in hospital responses to the Centers for Medicare and Medicaid's expansion of allowable secondary diagnoses in January 2011 and its association with financial penalties under the Hospital Readmission Reduction Program (HRRP). DATA SOURCES/STUDY SETTING: Medicare administrative claims for discharges between July 2008 and June 2011 (N=3102 hospitals). RESEARCH DESIGN: We examined hospital variation in response to the expansion of secondary diagnoses by describing changes in comorbidity coding before and after the policy change. We used random forest machine learning regression to examine hospital characteristics associated with coded severity. We then used a 2-part model to assess whether variation in coded severity was associated with readmission penalties. RESULTS: Changes in severity coding varied considerably across hospitals. Random forest models indicated that greater baseline levels of condition categories, case-mix index, and hospital size were associated with larger changes in condition categories. Hospital coding of an additional condition category was associated with a nonsignificant 3.8 percentage point increase in the probability for penalties under the HRRP (SE=2.2) and a nonsignificant 0.016 percentage point increase in penalty amount (SE=0.016). CONCLUSION: Changes in patient coded severity did not affect readmission penalties.


Assuntos
Centers for Medicare and Medicaid Services, U.S./normas , Codificação Clínica/estatística & dados numéricos , Aprendizado de Máquina , Readmissão do Paciente/estatística & dados numéricos , Grupos Diagnósticos Relacionados , Número de Leitos em Hospital/estatística & dados numéricos , Humanos , Revisão da Utilização de Seguros , Medicare/estatística & dados numéricos , Readmissão do Paciente/economia , Políticas , Índice de Gravidade de Doença , Estados Unidos
19.
Epidemiol Psychiatr Sci ; 29: e167, 2020 Sep 08.
Artigo em Inglês | MEDLINE | ID: mdl-32895087

RESUMO

AIMS: The number of mental hospital beds per population varies widely across countries, and the reasons for this variation are not fully understood. Given that differences in disease prevalence do not explain variation in inpatient mental health care availability, we examined the relationship between mental hospital beds and national income, education and longevity as measured by the Human Development Index (HDI). METHODS: We used an international dataset of social, economic and structural measures to conduct a mixed-effects longitudinal regression of predictors of the number of mental hospital beds per 100 000 in the overall population for 86 countries for years 2005-2015. RESULTS: Our initial dataset contained 1881 observations consisting of 11 years of potential measurements across 171 countries. After eliminations based on missing data and subsequent imputation, the dataset for the final regression model included 946 observations over 86 countries. The primary predictors of a country's number of mental hospital beds were year, HDI and GINI coefficient, the latter being a measure of income disparity. Holding all other factors constant, the number of beds decreased 8% per year, reflecting the ongoing international trend of deinstitutionalisation. As hypothesised, higher HDI predicted more mental hospital beds. Every 0.1 increase in HDI (0-1.0) was associated with a 126% increase in the number of hospital beds at the sample's mean GINI index score of 38 (0-100). However, a strong interaction between HDI and the GINI coefficient indicated that a high level of income disparity attenuated the positive association between HDI and mental hospital beds. At a GINI index score of 48, every 0.1 increase in HDI was associated with a 71% increase in the number of hospital beds. CONCLUSIONS: As countries reduce the number of hospital beds over time, higher levels of economic disparity are associated with a reduction in the strength of the association between national prosperity and investment in mental hospitals. As power becomes increasingly concentrated, perhaps those with the least are more easily forgotten.


Assuntos
Número de Leitos em Hospital/estatística & dados numéricos , Hospitais Psiquiátricos , Desenvolvimento Humano , Fatores Socioeconômicos , Ocupação de Leitos , Acesso aos Serviços de Saúde , Humanos
20.
s.l; Organización Panamericana de la Salud; ago. 11, 2020. 32 p.
Não convencional em Espanhol | LILACS | ID: biblio-1117100

RESUMO

A la fecha, se reportan 22.450 pacientes (29,4%) en aislamiento domiciliario, 1.669 pacientes (2,2%) se encuentran hospitalizados (1.509 en sala general y 160 en Unidades de Cuidado Intensivo -UCI). Se informan 50.665 casos (66,3%) como recuperados.


Assuntos
Humanos , Pneumonia Viral/epidemiologia , Ventiladores Mecânicos/estatística & dados numéricos , Infecções por Coronavirus/epidemiologia , Pandemias/estatística & dados numéricos , Betacoronavirus , Número de Leitos em Hospital/estatística & dados numéricos , Unidades de Terapia Intensiva/estatística & dados numéricos , Panamá/epidemiologia
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