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1.
PLoS One ; 17(1): e0262462, 2022.
Article in English | MEDLINE | ID: covidwho-1630364

ABSTRACT

Remdesivir and dexamethasone are the only drugs providing reductions in the lengths of hospital stays for COVID-19 patients. We assessed the impacts of remdesivir on hospital-bed resources and budgets affected by the COVID-19 outbreak. A stochastic agent-based model was combined with epidemiological data available on the COVID-19 outbreak in France and data from two randomized control trials. Strategies involving treating with remdesivir only patients with low-flow oxygen and patients with low-flow and high-flow oxygen were examined. Treating all eligible low-flow oxygen patients during the entirety of the second wave would have decreased hospital-bed occupancy in conventional wards by 4% [2%; 7%] and intensive care unit (ICU)-bed occupancy by 9% [6%; 13%]. Extending remdesivir use to high-flow-oxygen patients would have amplified reductions in ICU-bed occupancy by up to 14% [18%; 11%]. A minimum remdesivir uptake of 20% was required to observe decreases in bed occupancy. Dexamethasone had effects of similar amplitude. Depending on the treatment strategy, using remdesivir would, in most cases, generate savings (up to 722€) or at least be cost neutral (an extra cost of 34€). Treating eligible patients could significantly limit the saturation of hospital capacities, particularly in ICUs. The generated savings would exceed the costs of medications.


Subject(s)
Adenosine Monophosphate/analogs & derivatives , Alanine/analogs & derivatives , Antiviral Agents/economics , Bed Occupancy/economics , Dexamethasone/economics , Adenosine Monophosphate/economics , Adenosine Monophosphate/therapeutic use , Alanine/economics , Alanine/therapeutic use , Antiviral Agents/therapeutic use , Bed Occupancy/statistics & numerical data , COVID-19/drug therapy , COVID-19/economics , COVID-19/virology , Dexamethasone/therapeutic use , France , Hospitalization/economics , Hospitalization/statistics & numerical data , Humans , Intensive Care Units , Length of Stay , Models, Statistical , SARS-CoV-2/isolation & purification
2.
MMWR Morb Mortal Wkly Rep ; 70(46): 1613-1616, 2021 Nov 19.
Article in English | MEDLINE | ID: covidwho-1524681

ABSTRACT

Surges in COVID-19 cases have stressed hospital systems, negatively affected health care and public health infrastructures, and degraded national critical functions (1,2). Resource limitations, such as available hospital space, staffing, and supplies led some facilities to adopt crisis standards of care, the most extreme operating condition for hospitals, in which the focus of medical decision-making shifted from achieving the best outcomes for individual patients to addressing the immediate care needs of larger groups of patients (3). When hospitals deviated from conventional standards of care, many preventive and elective procedures were suspended, leading to the progression of serious conditions among some persons who would have benefitted from earlier diagnosis and intervention (4). During March-May 2020, U.S. emergency department visits declined by 23% for heart attacks, 20% for strokes, and 10% for diabetic emergencies (5). The Cybersecurity & Infrastructure Security Agency (CISA) COVID Task Force* examined the relationship between hospital strain and excess deaths during July 4, 2020-July 10, 2021, to assess the impact of COVID-19 surges on hospital system operations and potential effects on other critical infrastructure sectors and national critical functions. The study period included the months during which the highly transmissible SARS-CoV-2 B.1.617.2 (Delta) variant became predominant in the United States.† The negative binomial regression model used to calculate estimated deaths predicted that, if intensive care unit (ICU) bed use nationwide reached 75% capacity an estimated 12,000 additional excess deaths would occur nationally over the next 2 weeks. As hospitals exceed 100% ICU bed capacity, 80,000 excess deaths would be expected in the following 2 weeks. This analysis indicates the importance of controlling case growth and subsequent hospitalizations before severe strain. State, local, tribal, and territorial leaders could evaluate ways to reduce strain on public health and health care infrastructures, including implementing interventions to reduce overall disease prevalence such as vaccination and other prevention strategies, as well as ways to expand or enhance capacity during times of high disease prevalence.


Subject(s)
COVID-19/epidemiology , Hospitals/statistics & numerical data , Mortality/trends , Pandemics , Adult , Bed Occupancy/statistics & numerical data , COVID-19/mortality , COVID-19/therapy , Humans , Intensive Care Units/statistics & numerical data , United States/epidemiology
3.
PLoS One ; 16(10): e0257235, 2021.
Article in English | MEDLINE | ID: covidwho-1456081

ABSTRACT

During the early months of the current COVID-19 pandemic, social distancing measures effectively slowed disease transmission in many countries in Europe and Asia, but the same benefits have not been observed in some developing countries such as Brazil. In part, this is due to a failure to organise systematic testing campaigns at nationwide or even regional levels. To gain effective control of the pandemic, decision-makers in developing countries, particularly those with large populations, must overcome difficulties posed by an unequal distribution of wealth combined with low daily testing capacities. The economic infrastructure of these countries, often concentrated in a few cities, forces workers to travel from commuter cities and rural areas, which induces strong nonlinear effects on disease transmission. In the present study, we develop a smart testing strategy to identify geographic regions where COVID-19 testing could most effectively be deployed to limit further disease transmission. By smart testing we mean the testing protocol that is automatically designed by our optimization platform for a given time period, knowing the available number of tests, the current availability of ICU beds and the initial epidemiological situation. The strategy uses readily available anonymised mobility and demographic data integrated with intensive care unit (ICU) occupancy data and city-specific social distancing measures. Taking into account the heterogeneity of ICU bed occupancy in differing regions and the stages of disease evolution, we use a data-driven study of the Brazilian state of Sao Paulo as an example to show that smart testing strategies can rapidly limit transmission while reducing the need for social distancing measures, even when testing capacity is limited.


Subject(s)
Bed Occupancy/statistics & numerical data , COVID-19 Testing , COVID-19/diagnosis , COVID-19/prevention & control , Critical Care , COVID-19/epidemiology , Humans , Pandemics/prevention & control
4.
Am J Med ; 134(11): 1380-1388.e3, 2021 11.
Article in English | MEDLINE | ID: covidwho-1397151

ABSTRACT

BACKGROUND: Whether the volume of coronavirus disease 2019 (COVID-19) hospitalizations is associated with outcomes has important implications for the organization of hospital care both during this pandemic and future novel and rapidly evolving high-volume conditions. METHODS: We identified COVID-19 hospitalizations at US hospitals in the American Heart Association COVID-19 Cardiovascular Disease Registry with ≥10 cases between January and August 2020. We evaluated the association of COVID-19 hospitalization volume and weekly case growth indexed to hospital bed capacity, with hospital risk-standardized in-hospital case-fatality rate (rsCFR). RESULTS: There were 85 hospitals with 15,329 COVID-19 hospitalizations, with a median hospital case volume was 118 (interquartile range, 57, 252) and median growth rate of 2 cases per 100 beds per week but varied widely (interquartile range: 0.9 to 4.5). There was no significant association between overall hospital COVID-19 case volume and rsCFR (rho, 0.18, P = .09). However, hospitals with more rapid COVID-19 case-growth had higher rsCFR (rho, 0.22, P = 0.047), increasing across case growth quartiles (P trend = .03). Although there were no differences in medical treatments or intensive care unit therapies (mechanical ventilation, vasopressors), the highest case growth quartile had 4-fold higher odds of above median rsCFR, compared with the lowest quartile (odds ratio, 4.00; 1.15 to 13.8, P = .03). CONCLUSIONS: An accelerated case growth trajectory is a marker of hospitals at risk of poor COVID-19 outcomes, identifying sites that may be targets for influx of additional resources or triage strategies. Early identification of such hospital signatures is essential as our health system prepares for future health challenges.


Subject(s)
Bed Occupancy/statistics & numerical data , COVID-19 , Hospital Bed Capacity/statistics & numerical data , Intensive Care Units/statistics & numerical data , Mortality , Quality Improvement/organization & administration , COVID-19/mortality , COVID-19/therapy , Civil Defense , Health Care Rationing/organization & administration , Health Care Rationing/standards , Hospital Mortality , Hospitalization/statistics & numerical data , Humans , Outcome Assessment, Health Care , Registries , Risk Assessment , SARS-CoV-2 , Triage/organization & administration , United States/epidemiology
5.
BMC Med ; 19(1): 213, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1379790

ABSTRACT

BACKGROUND: The literature paints a complex picture of the association between mortality risk and ICU strain. In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain. METHODS: A national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease). RESULTS: One hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy (< 45% relative to the baseline) [OR 0.83 (95% PCI 0.75 to 0.94)]. CONCLUSION: Increasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Further research is required to establish if this is a causal relationship or whether it reflects strain on other operational factors such as staff. If causal, the result highlights the importance of strategies to keep ICU occupancy low to mitigate the impact of this type of resource saturation.


Subject(s)
Bed Occupancy/statistics & numerical data , COVID-19/mortality , Cause of Death , Critical Care/statistics & numerical data , Hospital Mortality , Intensive Care Units , Ventilators, Mechanical , Adolescent , Adult , Aged , Aged, 80 and over , Bayes Theorem , Female , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , Young Adult
6.
Epidemiol Infect ; 149: e102, 2021 04 27.
Article in English | MEDLINE | ID: covidwho-1279797

ABSTRACT

Estimating the lengths-of-stay (LoS) of hospitalised COVID-19 patients is key for predicting the hospital beds' demand and planning mitigation strategies, as overwhelming the healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the LoS in the intensive care unit (ICU), requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, such as the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge. Using real-time surveillance data from the first weeks of the COVID-19 epidemic in Galicia (Spain), we aimed to model the time-to-event and event probabilities of patients' hospitalised, without parametric priors and adjusting for individual covariates. We applied a non-parametric mixture cure model and compared its performance in estimating hospital ward (HW)/ICU LoS to the performances of commonly used methods to estimate survival. We showed that the proposed model outperformed standard approaches, providing more accurate ICU and HW LoS estimates. Finally, we applied our model estimates to simulate COVID-19 hospital demand using a Monte Carlo algorithm. We provided evidence that adjusting for sex, generally overlooked in prediction models, together with age is key for accurately forecasting HW and ICU occupancy, as well as discharge or death outcomes.


Subject(s)
COVID-19/epidemiology , Forecasting/methods , Length of Stay/trends , Models, Statistical , Age Factors , Bed Occupancy/statistics & numerical data , Bed Occupancy/trends , Hospital Mortality/trends , Hospitals , Humans , Intensive Care Units/statistics & numerical data , Intensive Care Units/trends , Length of Stay/statistics & numerical data , Patient Discharge/statistics & numerical data , Patient Discharge/trends , SARS-CoV-2 , Sex Factors , Spain/epidemiology , Statistics, Nonparametric , Survival Analysis
7.
Chest ; 161(1): 121-129, 2022 01.
Article in English | MEDLINE | ID: covidwho-1272334

ABSTRACT

BACKGROUND: During the first wave of the COVID-19 pandemic, shortages of ventilators and ICU beds overwhelmed health care systems. Whether early tracheostomy reduces the duration of mechanical ventilation and ICU stay is controversial. RESEARCH QUESTION: Can failure-free day outcomes focused on ICU resources help to decide the optimal timing of tracheostomy in overburdened health care systems during viral epidemics? STUDY DESIGN AND METHODS: This retrospective cohort study included consecutive patients with COVID-19 pneumonia who had undergone tracheostomy in 15 Spanish ICUs during the surge, when ICU occupancy modified clinician criteria to perform tracheostomy in Patients with COVID-19. We compared ventilator-free days at 28 and 60 days and ICU- and hospital bed-free days at 28 and 60 days in propensity score-matched cohorts who underwent tracheostomy at different timings (≤ 7 days, 8-10 days, and 11-14 days after intubation). RESULTS: Of 1,939 patients admitted with COVID-19 pneumonia, 682 (35.2%) underwent tracheostomy, 382 (56%) within 14 days. Earlier tracheostomy was associated with more ventilator-free days at 28 days (≤ 7 days vs > 7 days [116 patients included in the analysis]: median, 9 days [interquartile range (IQR), 0-15 days] vs 3 days [IQR, 0-7 days]; difference between groups, 4.5 days; 95% CI, 2.3-6.7 days; 8-10 days vs > 10 days [222 patients analyzed]: 6 days [IQR, 0-10 days] vs 0 days [IQR, 0-6 days]; difference, 3.1 days; 95% CI, 1.7-4.5 days; 11-14 days vs > 14 days [318 patients analyzed]: 4 days [IQR, 0-9 days] vs 0 days [IQR, 0-2 days]; difference, 3 days; 95% CI, 2.1-3.9 days). Except hospital bed-free days at 28 days, all other end points were better with early tracheostomy. INTERPRETATION: Optimal timing of tracheostomy may improve patient outcomes and may alleviate ICU capacity strain during the COVID-19 pandemic without increasing mortality. Tracheostomy within the first work on a ventilator in particular may improve ICU availability.


Subject(s)
COVID-19/therapy , Intensive Care Units , Pneumonia, Viral/therapy , Respiration, Artificial , Tracheostomy , Aged , Bed Occupancy/statistics & numerical data , COVID-19/epidemiology , Female , Humans , Length of Stay/statistics & numerical data , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Propensity Score , Retrospective Studies , Spain/epidemiology
8.
Ciênc. Saúde Colet ; 26(4): 1441-1456, abr. 2021. tab, graf
Article in Portuguese | WHO COVID, LILACS (Americas) | ID: covidwho-1216975

ABSTRACT

Resumo Mesmo no período em que a pandemia de Covid-19 encontrava-se em crescimento no Nordeste do Brasil, iniciou-se a adoção de medidas de flexibilização do distanciamento social. O objetivo do estudo é o de avaliar a pertinência das propostas de flexibilização, tomando-se em conta a situação da pandemia em cada local e o momento em que foram adotadas. Tendo como referência as diretrizes da OMS, foram construídos e analisados indicadores operacionais para cada diretriz, no contexto da região Nordeste. Para análise do comportamento da epidemia, conforme indicadores selecionados, foram usadas técnicas de Joinpoint Trend Analysis, mapas de calor, razão de taxas e comparação da tendência temporal entre capitais e interior dos estados. O pico do crescimento semanal ocorreu em maio-julho/2020 (semanas epidemiológicas 19 a 31). Na maioria das capitais não se observou tendência decrescente simultânea do número de casos e óbitos nos 14 dias prévios à flexibilização. Em todos os estados o quantitativo de testes realizados foi insuficiente. Na semana epidemiológica 24 os percentuais estaduais de ocupação de leitos de UTI/Covid-19 foram próximos ou superiores 70%. A situação epidemiológica das nove capitais dos estados do Nordeste, no momento em que a decisão de flexibilização foi tomada, mostra que nenhuma delas atendia aos critérios e parâmetros recomendados pela OMS.


Abstract Even in the period when the Covid-19 pandemic was on the rise in the Northeast of Brazil, the relaxation of social distancing measures was introduced. The scope of the study is to assess, in the light of the epidemiological-sanitary situation in the region, the suitability of relaxation of social distancing measures. Based on the WHO guidelines for relaxation of social distancing, operational indicators were created and analyzed for each guideline in the context of the Northeast. To analyze the behavior of the epidemic, according to selected indicators, Joinpoint trend analysis techniques, heat maps, rate ratios and time trends between capitals and the state interior were compared. The weekly growth peak of the epidemic occurred in May-July 2020 (epidemiological weeks 19 to 31). In most capitals, there was no simultaneous downward trend in the number of cases and deaths in the 14 days prior to flexibilization. In all states the number of tests performed was insufficient. In epidemiological week 24, the state percentages of ICU/Covid-19 bed occupancy were close to or above 70%. The epidemiological situation of the nine Northeastern state capitals analyzed here did not meet criteria and parameters recommended by the World Health Organization for the relaxation of social distancing measures.


Subject(s)
Humans , Pandemics , Physical Distancing , COVID-19/epidemiology , Bed Occupancy/statistics & numerical data , World Health Organization , Brazil/epidemiology , Communicable Disease Control , COVID-19/prevention & control
10.
Pediatr Ann ; 50(4): e172-e177, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1211970

ABSTRACT

Severe acute respiratory syndrome coronavirus 2, the virus causing the pandemic illness coronavirus disease 2019, was first detected in the United States in January 2020. As the illness spread across the country, all aspects and venues of health care were significantly impacted. This article explores the challenges and response of one children's emergency medicine division related to surge planning, personal protective equipment, screening, testing, staffing, and other operational challenges, and describes the impact and implications thus far. [Pediatr Ann. 2021;50(4):e172-e177.].


Subject(s)
COVID-19/diagnosis , COVID-19/therapy , Emergency Service, Hospital , Bed Occupancy/statistics & numerical data , Child , Humans , Personal Protective Equipment , Personnel Staffing and Scheduling , SARS-CoV-2 , United States
11.
S Afr Med J ; 111(3): 240-244, 2021 03 02.
Article in English | MEDLINE | ID: covidwho-1168067

ABSTRACT

BACKGROUND: The COVID-19 pandemic has impacted on the global surgery landscape. OBJECTIVES: To analyse and describe the initial impact of the COVID-19 pandemic on orthopaedic surgery at Groote Schuur Hospital, a tertiary academic hospital in South Africa. METHODS: The number of orthopaedic surgical cases, emergency theatre patient waiting times, and numbers of outpatient clinic visits, ward admissions, bed occupancies and total inpatient days for January - April 2019 (pre-COVID-19) were compared with the same time frame in 2020 (COVID-19). The COVID-19 timeframe included initiation of a national 'hard lockdown' from 26 March 2020, in preparation for an increasing volume of COVID-19 cases. RESULTS: April 2020, the time of the imposed hard lockdown, was the most affected month, although the number of surgical cases had started to decrease slowly during the 3 preceding months. The total number of surgeries, outpatient visits and ward admissions decreased significantly during April 2020 (55.2%, 69.1% and 60.6%, respectively) compared with April 2019 (p<0.05). Trauma cases were reduced by 40% in April 2020. Overall emergency theatre patient waiting time was 30% lower for April 2020 compared with 2019. CONCLUSIONS: COVID-19 and the associated lockdown has heavily impacted on both orthopaedic inpatient and outpatient services. Lockdown led to a larger reduction in the orthopaedic trauma burden than in international centres, but the overall reduction in surgeries, outpatient visits and hospital admissions was less. This lesser reduction was probably due to local factors, but also to a conscious decision to avoid total collapse of our surgical services.


Subject(s)
COVID-19/epidemiology , Orthopedic Procedures/statistics & numerical data , Pneumonia, Viral/epidemiology , Ambulatory Care/statistics & numerical data , Bed Occupancy/statistics & numerical data , Hospitalization/statistics & numerical data , Hospitals, Urban , Humans , Length of Stay/statistics & numerical data , Pandemics , SARS-CoV-2 , South Africa/epidemiology , Tertiary Care Centers , Waiting Lists
12.
Biosci Trends ; 15(1): 1-8, 2021 Mar 15.
Article in English | MEDLINE | ID: covidwho-1154736

ABSTRACT

The first case of COVID-19 in Japan was reported on 16 January 2020. The total number of the infected has reached 313,844 and the number of deaths has reached 4,379 as of 16 January 2021. This article reviews the characteristics of and responses to the three waves of COVID-19 in Japan during 2020-2021 in order to provide a reference for the next step in epidemic prevention and control. The Japanese Government declared a state of emergency on 7 April 2020, which suppressed the increase in the number of the infected by curtailing economic activity. The first wave peaked at 701 new cases a day and it decreased to 21 new cases on May 25 when the state of emergency was lifted. However, the number of the infected increased again due to the resumption of economic activity, with a peak of 1,762 new cases a day during the second wave. Although the situation was worse than that during the first wave, the government succeeded in limiting the increase without declaring a state of emergency again, and that may be attributed to a decrease in crowd activities and an increase in the number of inspections. During the third wave, the number of the infected continued to exceed the peak during previous waves for two months. Major factors for this rise include the government's implementation of further policies to encourage certain activities, relaxed immigration restrictions, and people not reducing their level of activity. An even more serious problem is the bed usage for patients with COVID-19; bed usage exceeds 50% not only in major cities but also in various areas. On 7 January 2021, 5,953 new cases were reported a day; this greatly exceeded the previous peak, and the state of emergency was declared again. Although Japan has been preparing its medical system since the first wave, maintaining that system has imposed a large economic burden on medical facilities, hence stronger measures and additional support are urgently needed to combat COVID-19 in the coming few months.


Subject(s)
COVID-19 , Disease Outbreaks/statistics & numerical data , Bed Occupancy/statistics & numerical data , Humans , Japan/epidemiology
13.
Säo Paulo med. j ; 139(2): 178-185, Mar.-Apr. 2021. tab, graf
Article in English | WHO COVID, LILACS (Americas) | ID: covidwho-1138617

ABSTRACT

ABSTRACT BACKGROUND: The fragility of healthcare systems worldwide had not been exposed by any pandemic until now. The lack of integrated methods for bed capacity planning compromises the effectiveness of public and private hospitals' services. OBJECTIVES: To estimate the impact of the COVID-19 pandemic on the provision of intensive care unit and clinical beds for Brazilian states, using an integrated model. DESIGN AND SETTING: Experimental study applying healthcare informatics to data on COVID-19 cases from the official electronic platform of the Brazilian Ministry of Health. METHODS: A predictive model based on the historical records of Brazilian states was developed to estimate the need for hospital beds during the COVID-19 pandemic. RESULTS: The proposed model projected in advance that there was a lack of 22,771 hospital beds for Brazilian states, of which 38.95% were ICU beds, and 61.05% were clinical beds. CONCLUSIONS: The proposed approach provides valuable information to help hospital managers anticipate actions for improving healthcare system capacity.


Subject(s)
Humans , Bed Occupancy/statistics & numerical data , Pandemics , COVID-19 , Intensive Care Units/statistics & numerical data , Brazil/epidemiology , SARS-CoV-2 , Hospitals
14.
Bol Med Hosp Infant Mex ; 78(1): 3-9, 2020 11 23.
Article in English | MEDLINE | ID: covidwho-1138997

ABSTRACT

Overview of the pandemic In December 2019, a new virus named SARS-CoV-2 was reported in Wuhan province, China. The first case of COVID-19 in Mexico was confirmed on February 28, 2020, and the World Health Organization declared the pandemic on March 11.


Subject(s)
Bed Occupancy/statistics & numerical data , COVID-19/epidemiology , Hospitals, Pediatric/organization & administration , Pandemics , Algorithms , Health Personnel , Humans , Mexico , Needs Assessment , Triage , Workforce
16.
Int J Qual Health Care ; 33(1)2021 Mar 03.
Article in English | MEDLINE | ID: covidwho-1096534

ABSTRACT

BACKGROUND: The effects of an early and prolonged lockdown during the coronavirus disease 2019 (COVID-19) pandemic on cardiovascular intensive care units (CICUs) are not well established. OBJECTIVES: This study analyses patterns of admission, mortality and performance indicators in a CICU before and during the Argentine lockdown in the COVID-19 pandemic. METHODS: This is a retrospective observational cross-sectional study of all consecutive patients aged 18 years or more admitted to the cardiac intensive care unit at a high-volume reference hospital in Buenos Aires, Argentina, comparing hospitalization rates, primary causes of admission, inpatient utilization indicators, pharmacy supplies' expenditures and in-hospital mortality between 5 March and 31 July 2020, with two corresponding control periods in 2019 and 2018. RESULTS: We included 722 female patients [mean age of 61.6 (SD 15.5) years; 237 (32.8%)]. Overall hospitalizations dropped 53.2% (95%CI: 45.3, 61.0%), from 295.5 patients/year over the periods 2018/2019 to 137 patients in 2020. Cardiovascular disease-related admissions dropped 59.9%, while admission for non-cardiac causes doubled its prevalence from 9.6% over the periods 2018/2019 to 22.6% in the study period (P < 0.001).In the period 2020, the bed occupancy rate fell from 82.2% to 77.4%, and the bed turnover rate dropped 50% from 7.88 to 3.91 monthly discharges/bed. The average length of stay doubled from 3.26 to 6.75 days, and the turnover interval increased from 3.8 to 8.39 days in 2020.Pharmacy supplies' expenditures per discharge increased 134% along with a rise in antibiotics usage from 6.5 to 11.4 vials/ampoules per discharge (P < 0.02).Overall mortality increased from 7% (n = 41) to 13.9% (n = 19) (P = 0.008) at the expense of non-cardiac-related admissions (3.6-19.4%, P = 0.01). CONCLUSIONS: This study found a significant reduction in overall and cardiovascular disease-related causes of admission to the cardiac intensive care unit, worse performance indicators and increased in-hospital mortality along the first 5 months of the early and long-lasting COVID-19 lockdown in Argentina. These results highlight the need to foster public awareness concerning the risks of avoiding hospital attendance. Moreover, health systems should follow strict screening protocols to prevent potential biases in the admission of patients with critical conditions unrelated to the COVID-19 pandemic.


Subject(s)
COVID-19/epidemiology , Cardiovascular Diseases/epidemiology , Intensive Care Units/statistics & numerical data , Patient Admission/statistics & numerical data , Quality Indicators, Health Care/statistics & numerical data , Adult , Aged , Argentina/epidemiology , Bed Occupancy/statistics & numerical data , Cross-Sectional Studies , Female , Health Policy , Hospital Mortality/trends , Humans , Length of Stay/statistics & numerical data , Male , Middle Aged , Pandemics , Pharmacy Service, Hospital/economics , Pharmacy Service, Hospital/statistics & numerical data , Retrospective Studies , SARS-CoV-2
17.
Public Health ; 193: 41-42, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1078141

ABSTRACT

OBJECTIVES: Identification of environmental and hospital indicators that may influence coronavirus disease 2019 (COVID-19) mortality in different countries is essential for better management of this infectious disease. STUDY DESIGN: Correlation analysis between healthcare system indicators and COVID-19 mortality rate in Europe. METHODS: For each country in the European Union (EU), the date of the first diagnosed case and the crude death rate for COVID-19 were retrieved from the John Hopkins University website. These data were then combined with environmental, hospital and clinical indicators extracted from the European Health Information Gateway of the World Health Organization. RESULTS: The COVID-19 death rate in EU countries (mean 1.9 ± 0.8%) was inversely associated with the number of available general hospitals, physicians and nurses. Significant positive associations were also found with the rate of acute care bed occupancy, as well as with the proportion of population who were aged older than 65 years, overweight or who had cancer. Total healthcare expenditure, public sector health expenditure and the number of hospital and acute care beds did not influence COVID-19 death rate. CONCLUSIONS: Some common healthcare system inadequacies, such as limited numbers of general hospitals, physicians and nurses, in addition to high acute care bed occupancy, may be significant drivers of nationwide COVID-19 mortality rates in EU countries.


Subject(s)
COVID-19/mortality , European Union/statistics & numerical data , Quality Indicators, Health Care , Aged , Bed Occupancy/statistics & numerical data , COVID-19/therapy , Humans
18.
Int J Health Policy Manag ; 9(11): 469-474, 2020 11 01.
Article in English | MEDLINE | ID: covidwho-1068324

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the current pandemic of coronavirus disease 2019 (COVID-19). This pandemic is characterized by a high variability in death rate (defined as the ratio between the number of deaths and the total number of infected people) across world countries. Several possible explanations have been proposed, but it is not clear whether this variability is due to a single predominant factor or instead to multiple causes. Here we addressed this issue using multivariable regression analysis to test the impact of the following factors: the hospital stress (defined as the ratio between the number of infected cases and the total number of hospital beds), the population median age, and the quality of the National Health System (NHS). For this analysis, we chose countries of the world with over 3000 infected cases as of April 1, 2020. Hospital stress was found to be the crucial factor in explaining the variability of death rate, while the others had negligible relevance. Different procedures for quantifying cases of infection and death for COVID-19 could affect the variability in death rate across countries. We therefore applied the same statistical approach to Italy, which is divided into 20 Regions that share the same protocol for counting the outcomes of this pandemic. Correlation between hospital stress and death rate was even stronger than that observed for countries of the world. Based on our findings and the historical trend for the availability of hospital beds, we propose guidelines for policy-makers to properly manage future pandemics.


Subject(s)
Bed Occupancy/statistics & numerical data , COVID-19/epidemiology , Pandemics/statistics & numerical data , Humans , Internationality , SARS-CoV-2
20.
BMJ Open ; 11(1): e042945, 2021 01 26.
Article in English | MEDLINE | ID: covidwho-1050402

ABSTRACT

OBJECTIVE: In this study, we describe the pattern of bed occupancy across England during the peak of the first wave of the COVID-19 pandemic. DESIGN: Descriptive survey. SETTING: All non-specialist secondary care providers in England from 27 March27to 5 June 2020. PARTICIPANTS: Acute (non-specialist) trusts with a type 1 (ie, 24 hours/day, consultant-led) accident and emergency department (n=125), Nightingale (field) hospitals (n=7) and independent sector secondary care providers (n=195). MAIN OUTCOME MEASURES: Two thresholds for 'safe occupancy' were used: 85% as per the Royal College of Emergency Medicine and 92% as per NHS Improvement. RESULTS: At peak availability, there were 2711 additional beds compatible with mechanical ventilation across England, reflecting a 53% increase in capacity, and occupancy never exceeded 62%. A consequence of the repurposing of beds meant that at the trough there were 8.7% (8508) fewer general and acute beds across England, but occupancy never exceeded 72%. The closest to full occupancy of general and acute bed (surge) capacity that any trust in England reached was 99.8% . For beds compatible with mechanical ventilation there were 326 trust-days (3.7%) spent above 85% of surge capacity and 154 trust-days (1.8%) spent above 92%. 23 trusts spent a cumulative 81 days at 100% saturation of their surge ventilator bed capacity (median number of days per trust=1, range: 1-17). However, only three sustainability and transformation partnerships (aggregates of geographically co-located trusts) reached 100% saturation of their mechanical ventilation beds. CONCLUSIONS: Throughout the first wave of the pandemic, an adequate supply of all bed types existed at a national level. However, due to an unequal distribution of bed utilisation, many trusts spent a significant period operating above 'safe-occupancy' thresholds despite substantial capacity in geographically co-located trusts, a key operational issue to address in preparing for future waves.


Subject(s)
COVID-19/epidemiology , Hospital Bed Capacity , Hospitals/supply & distribution , Surge Capacity , Ventilators, Mechanical/supply & distribution , Bed Occupancy/statistics & numerical data , England/epidemiology , Health Personnel , Humans , Intensive Care Units/supply & distribution , SARS-CoV-2 , State Medicine
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