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1.
Ann Intern Med ; 174(9): 1240-1251, 2021 09.
Article in English | MEDLINE | ID: covidwho-1789654

ABSTRACT

BACKGROUND: Several U.S. hospitals had surges in COVID-19 caseload, but their effect on COVID-19 survival rates remains unclear, especially independent of temporal changes in survival. OBJECTIVE: To determine the association between hospitals' severity-weighted COVID-19 caseload and COVID-19 mortality risk and identify effect modifiers of this relationship. DESIGN: Retrospective cohort study. (ClinicalTrials.gov: NCT04688372). SETTING: 558 U.S. hospitals in the Premier Healthcare Database. PARTICIPANTS: Adult COVID-19-coded inpatients admitted from March to August 2020 with discharge dispositions by October 2020. MEASUREMENTS: Each hospital-month was stratified by percentile rank on a surge index (a severity-weighted measure of COVID-19 caseload relative to pre-COVID-19 bed capacity). The effect of surge index on risk-adjusted odds ratio (aOR) of in-hospital mortality or discharge to hospice was calculated using hierarchical modeling; interaction by surge attributes was assessed. RESULTS: Of 144 116 inpatients with COVID-19 at 558 U.S. hospitals, 78 144 (54.2%) were admitted to hospitals in the top surge index decile. Overall, 25 344 (17.6%) died; crude COVID-19 mortality decreased over time across all surge index strata. However, compared with nonsurging (<50th surge index percentile) hospital-months, aORs in the 50th to 75th, 75th to 90th, 90th to 95th, 95th to 99th, and greater than 99th percentiles were 1.11 (95% CI, 1.01 to 1.23), 1.24 (CI, 1.12 to 1.38), 1.42 (CI, 1.27 to 1.60), 1.59 (CI, 1.41 to 1.80), and 2.00 (CI, 1.69 to 2.38), respectively. The surge index was associated with mortality across ward, intensive care unit, and intubated patients. The surge-mortality relationship was stronger in June to August than in March to May (slope difference, 0.10 [CI, 0.033 to 0.16]) despite greater corticosteroid use and more judicious intubation during later and higher-surging months. Nearly 1 in 4 COVID-19 deaths (5868 [CI, 3584 to 8171]; 23.2%) was potentially attributable to hospitals strained by surging caseload. LIMITATION: Residual confounding. CONCLUSION: Despite improvements in COVID-19 survival between March and August 2020, surges in hospital COVID-19 caseload remained detrimental to survival and potentially eroded benefits gained from emerging treatments. Bolstering preventive measures and supporting surging hospitals will save many lives. PRIMARY FUNDING SOURCE: Intramural Research Program of the National Institutes of Health Clinical Center, the National Institute of Allergy and Infectious Diseases, and the National Cancer Institute.


Subject(s)
COVID-19/mortality , Hospitalization/statistics & numerical data , Adrenal Cortex Hormones/therapeutic use , Adult , COVID-19/therapy , Critical Care/statistics & numerical data , Female , Hospital Bed Capacity/statistics & numerical data , Hospital Mortality , Humans , Male , Odds Ratio , Respiration, Artificial , Retrospective Studies , Risk Assessment , Risk Factors , SARS-CoV-2 , Survival Rate , United States/epidemiology
2.
Ciênc. Saúde Colet ; 25(supl.1): 2461-2468, Mar. 2020. graf
Article in Portuguese | WHO COVID, LILACS (Americas) | ID: covidwho-1725050

ABSTRACT

Resumo A distribuição geográfica da COVID-19 por meio de recursos de Sistemas de Informação Geográfica é pouco explorada. O objetivo foi analisar a distribuição de casos da COVID-19 e de leitos de terapia intensiva exclusivos para a doença no estado do Ceará, Brasil. Estudo ecológico, com distribuição geográfica do coeficiente de detecção de casos da doença em 184 municípios. Construíram-se mapas dos valores brutos e estimados (método bayesiano global e local), com cálculo do índice de Moran e utilização do "BoxMap" e "MoranMap" Os leitos foram distribuídos por meio de pontos geolocalizados. Estudaram-se 3.000 casos e 459 leitos. As maiores taxas encontram-se na capital Fortaleza, região metropolitana (RM) e ao sul dessa região. Há autocorrelação espacial positiva na taxa bayesiana local (I = 0,66). A distribuição dos leitos de terapia intensiva sobreposta ao "BoxMap" evidenciou aglomerados com padrão Alto-Alto apresentando número de leitos (capital, RM, porção noroeste); porém, há o mesmo padrão (extremo leste) e em áreas de transição com insuficiência de leito. O "MoranMap" evidenciou "clusters" estatisticamente significativos no estado. A interiorização da COVID-19 no Ceará demanda medidas de contingência voltadas à distribuição dos leitos de terapia intensiva específicos para casos de COVID19 para atender à demanda.


Abstract 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.


Subject(s)
Humans , Pneumonia, Viral/epidemiology , Coronavirus Infections/epidemiology , Pandemics , Geographic Mapping , Betacoronavirus , Hospital Bed Capacity/statistics & numerical data , Intensive Care Units/supply & distribution , Pneumonia, Viral/transmission , Brazil/epidemiology , Bayes Theorem , Coronavirus Infections , Coronavirus Infections/transmission , Geographic Information Systems
3.
PLoS One ; 16(11): e0260310, 2021.
Article in English | MEDLINE | ID: covidwho-1523457

ABSTRACT

The first case of COVID-19 was detected in North Carolina (NC) on March 3, 2020. By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19's impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. Here we describe key steps taken, challenges faced, and lessons learned from adapting and implementing our COVID-19 model and coordinating with university, state, and federal partners to combat the COVID-19 epidemic in NC.


Subject(s)
COVID-19/epidemiology , Hospital Bed Capacity/statistics & numerical data , Hospitalization/trends , Intensive Care Units/trends , Pandemics/statistics & numerical data , Delivery of Health Care , Forecasting , Humans , North Carolina/epidemiology
5.
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
7.
Ann Intern Med ; 174(9): 1240-1251, 2021 09.
Article in English | MEDLINE | ID: covidwho-1296184

ABSTRACT

BACKGROUND: Several U.S. hospitals had surges in COVID-19 caseload, but their effect on COVID-19 survival rates remains unclear, especially independent of temporal changes in survival. OBJECTIVE: To determine the association between hospitals' severity-weighted COVID-19 caseload and COVID-19 mortality risk and identify effect modifiers of this relationship. DESIGN: Retrospective cohort study. (ClinicalTrials.gov: NCT04688372). SETTING: 558 U.S. hospitals in the Premier Healthcare Database. PARTICIPANTS: Adult COVID-19-coded inpatients admitted from March to August 2020 with discharge dispositions by October 2020. MEASUREMENTS: Each hospital-month was stratified by percentile rank on a surge index (a severity-weighted measure of COVID-19 caseload relative to pre-COVID-19 bed capacity). The effect of surge index on risk-adjusted odds ratio (aOR) of in-hospital mortality or discharge to hospice was calculated using hierarchical modeling; interaction by surge attributes was assessed. RESULTS: Of 144 116 inpatients with COVID-19 at 558 U.S. hospitals, 78 144 (54.2%) were admitted to hospitals in the top surge index decile. Overall, 25 344 (17.6%) died; crude COVID-19 mortality decreased over time across all surge index strata. However, compared with nonsurging (<50th surge index percentile) hospital-months, aORs in the 50th to 75th, 75th to 90th, 90th to 95th, 95th to 99th, and greater than 99th percentiles were 1.11 (95% CI, 1.01 to 1.23), 1.24 (CI, 1.12 to 1.38), 1.42 (CI, 1.27 to 1.60), 1.59 (CI, 1.41 to 1.80), and 2.00 (CI, 1.69 to 2.38), respectively. The surge index was associated with mortality across ward, intensive care unit, and intubated patients. The surge-mortality relationship was stronger in June to August than in March to May (slope difference, 0.10 [CI, 0.033 to 0.16]) despite greater corticosteroid use and more judicious intubation during later and higher-surging months. Nearly 1 in 4 COVID-19 deaths (5868 [CI, 3584 to 8171]; 23.2%) was potentially attributable to hospitals strained by surging caseload. LIMITATION: Residual confounding. CONCLUSION: Despite improvements in COVID-19 survival between March and August 2020, surges in hospital COVID-19 caseload remained detrimental to survival and potentially eroded benefits gained from emerging treatments. Bolstering preventive measures and supporting surging hospitals will save many lives. PRIMARY FUNDING SOURCE: Intramural Research Program of the National Institutes of Health Clinical Center, the National Institute of Allergy and Infectious Diseases, and the National Cancer Institute.


Subject(s)
COVID-19/mortality , Hospitalization/statistics & numerical data , Adrenal Cortex Hormones/therapeutic use , Adult , COVID-19/therapy , Critical Care/statistics & numerical data , Female , Hospital Bed Capacity/statistics & numerical data , Hospital Mortality , Humans , Male , Odds Ratio , Respiration, Artificial , Retrospective Studies , Risk Assessment , Risk Factors , SARS-CoV-2 , Survival Rate , United States/epidemiology
8.
Dig Surg ; 38(4): 259-265, 2021.
Article in English | MEDLINE | ID: covidwho-1247450

ABSTRACT

BACKGROUND: The first COVID-19 pandemic wave hit most of the health-care systems worldwide. The present survey aimed to provide a European overview on the COVID-19 impact on surgical oncology. METHODS: This anonymous online survey was accessible from April 24 to May 11, 2020, for surgeons (n = 298) who were contacted by the surgical society European Digestive Surgery. The survey was completed by 88 surgeons (29.2%) from 69 different departments. The responses per department were evaluated. RESULTS: Of the departments, 88.4% (n = 61/69) reported a lower volume of patients in the outpatient clinic; 69.1% (n = 47/68) and 75.0% (n = 51/68) reported a reduction in hospital bed and the operating room capacity, respectively. As a result, the participants reported an average reduction of 29.3% for all types of oncological resections surveyed in this questionnaire. The strongest reduction was observed for oncological resections of hepato-pancreatico-biliary (HPB) cancers. Of the interviewed surgeons, 68.7% (n = 46/67) agreed that survival outcomes will be negatively impacted by the pandemic. CONCLUSION: The first COVID-19 pandemic wave had a significant impact on surgical oncology in Europe. The surveyed surgeons expect an increase in the number of unresectable cancers as well as poorer survival outcomes due to cancellations of follow-ups and postponements of surgeries.


Subject(s)
COVID-19/epidemiology , Hospital Bed Capacity/statistics & numerical data , Neoplasms/surgery , Oncology Service, Hospital/statistics & numerical data , Surgical Oncology/statistics & numerical data , Adult , Ambulatory Care/statistics & numerical data , COVID-19/diagnosis , Chemotherapy, Adjuvant/statistics & numerical data , Cross-Sectional Studies , Europe/epidemiology , Female , Humans , Male , Middle Aged , Neoplasms/diagnosis , Neoplasms/drug therapy , Operating Rooms/statistics & numerical data , Surveys and Questionnaires , Survival Rate , Time-to-Treatment/statistics & numerical data
11.
Am J Public Health ; 111(5): 923-926, 2021 05.
Article in English | MEDLINE | ID: covidwho-1177869

ABSTRACT

Objectives. To estimate the critical care bed capacity that would be required to admit all critical COVID-19 cases in a setting of unchecked SARS-CoV-2 transmission, both with and without elderly-specific protection measures.Methods. Using electronic health records of all 2432 COVID-19 patients hospitalized in a large hospital in Madrid, Spain, between February 28 and April 23, 2020, we estimated the number of critical care beds needed to admit all critical care patients. To mimic a hypothetical intervention that halves SARS-CoV-2 infections among the elderly, we randomly excluded 50% of patients aged 65 years and older.Results. Critical care requirements peaked at 49 beds per 100 000 on April 1-2 weeks after the start of a national lockdown. After randomly excluding 50% of elderly patients, the estimated peak was 39 beds per 100 000.Conclusions. Under unchecked SARS-CoV-2 transmission, peak critical care requirements in Madrid were at least fivefold higher than prepandemic capacity. Under a hypothetical intervention that halves infections among the elderly, critical care peak requirements would have exceeded the prepandemic capacity of most high-income countries.Public Health Implications. Pandemic control strategies that rely exclusively on protecting the elderly are likely to overwhelm health care systems.


Subject(s)
COVID-19 , Communicable Disease Control , Critical Care , Hospital Bed Capacity/statistics & numerical data , Hospitalization , Adult , Aged , COVID-19/epidemiology , COVID-19/transmission , Electronic Health Records , Female , Humans , Male , Middle Aged , Models, Statistical , Spain/epidemiology , Young Adult
12.
Public Health ; 194: 135-142, 2021 May.
Article in English | MEDLINE | ID: covidwho-1142206

ABSTRACT

OBJECTIVES: The purpose of this study was to determine predictors of the height of coronavirus disease 2019 (COVID-19) daily deaths' peak and time to the peak, to explain their variability across European countries. STUDY DESIGN: For 34 European countries, publicly available data were collected on daily numbers of COVID-19 deaths, population size, healthcare capacity, government restrictions and their timing, tourism and change in mobility during the pandemic. METHODS: Univariate and multivariate generalised linear models using different selection algorithms (forward, backward, stepwise and genetic algorithm) were analysed with height of COVID-19 daily deaths' peak and time to the peak as dependent variables. RESULTS: The proportion of the population living in urban areas, mobility at the day of first reported death and number of infections when borders were closed were assessed as significant predictors of the height of COVID-19 daily deaths' peak. Testing the model with a variety of selection algorithms provided consistent results. Total hospital bed capacity, population size, the number of foreign travellers and the day of border closure were found to be significant predictors of time to COVID-19 daily deaths' peak. CONCLUSIONS: Our analysis demonstrated that countries with higher proportions of the population living in urban areas, countries with lower reduction in mobility at the beginning of the pandemic and countries having more infected people when closing borders experienced a higher peak of COVID-19 deaths. Greater bed capacity, bigger population size and later border closure could result in delaying time to reach the deaths' peak, whereas a high number of foreign travellers could accelerate it.


Subject(s)
COVID-19/mortality , Adult , Europe/epidemiology , Hospital Bed Capacity/statistics & numerical data , Humans , Linear Models , Pandemics , Population Density , SARS-CoV-2 , Travel , Urban Population/statistics & numerical data
13.
J Hosp Med ; 16(4): 215-218, 2021 04.
Article in English | MEDLINE | ID: covidwho-1140804

ABSTRACT

Some hospitals have faced a surge of patients with COVID-19, while others have not. We assessed whether COVID-19 burden (number of patients with COVID-19 admitted during April 2020 divided by hospital certified bed count) was associated with mortality in a large sample of US hospitals. Our study population included 14,226 patients with COVID-19 (median age 66 years, 45.2% women) at 117 hospitals, of whom 20.9% had died at 5 weeks of follow-up. At the hospital level, the observed mortality ranged from 0% to 44.4%. After adjustment for age, sex, and comorbidities, the adjusted odds ratio for in-hospital death in the highest quintile of burden was 1.46 (95% CI, 1.07-2.00) compared to all other quintiles. Still, there was large variability in outcomes, even among hospitals with a similar level of COVID-19 burden and after adjusting for age, sex, and comorbidities.


Subject(s)
COVID-19/mortality , Hospital Bed Capacity/statistics & numerical data , Hospital Mortality/trends , Aged , Comorbidity/trends , Female , Hospitalization , Humans , Male , United States
14.
Colomb Med (Cali) ; 51(3): e204534, 2020 Sep 30.
Article in English | MEDLINE | ID: covidwho-1128318

ABSTRACT

BACKGROUND: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic. METHODS: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario. RESULTS: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%. CONCLUSION: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making.


INTRODUCCIÓN: Valle del Cauca es el departamento con el cuarto mayor número de casos de COVID-19 en Colombia (>50,000 en septiembre 7, 2020). Debido a la ausencia de tratamientos efectivos para COVID-19, los tomadores de decisiones requieren de acceso a información actualizada para estimar la incidencia de la enfermedad, y la disponibilidad de recursos hospitalarios para contener la pandemia. MÉTODOS: Adaptamos un modelo existente al contexto local para estimar la incidencia de COVID-19, y la demanda de recursos hospitalarios en los próximos meses. Para ello, modelamos cuatro escenarios hipotéticos: (1) el gobierno local implementa una cuarentena desde el primero de septiembre hasta el 15 de octubre (asumiendo una tasa promedio de infecciones diarias del 2%); (2-3) se implementan restricciones parciales (tasas de infección del 4% y 8%); (4) se levantan todas las restricciones (tasa del 10%). Los mismos escenarios fueron previamente evaluados entre julio y agosto, y los resultados fueron resumidos. Estimamos el número de casos nuevos, el número de pruebas diagnósticas requeridas, y el numero de camas de hospital y de unidad de cuidados intensivos (con y sin ventilación) disponibles, para cada escenario. RESULTADOS: El modelo estimó 67,700 casos a octubre 15 al asumir la implementación de una nueva cuarentena, 80,400 y 101,500 al asumir restricciones parciales al 4 y 8% de infecciones diarias, respectivamente, y 208,500 al asumir ninguna restricción. La demanda por pruebas diagnósticas (de reacción en cadena de la polimerasa) fue estimada entre 202,000 y 1,610,600 entre septiembre 1 y octubre 15, a través de los diferentes escenarios evaluados. El modelo estimó un agotamiento de camas de cuidados intensivos para septiembre 20 al asumir una tasa de infecciones del 10%. Conclusión: Se estima que el levantamiento paulatino de las restricciones de distanciamiento social y la reapertura de la economía no debería causar el agotamiento de recursos hospitalarios si la tasa de infección diaria se mantiene por debajo del 8%. Sin embargo, incrementar la disponibilidad de camas permitiría al sistema de salud ajustarse rápidamente a potenciales picos inesperados de infecciones nuevas. Los modelos de predicción deben ser utilizados de manera iterativa para depurar las predicciones epidemiológicas y para proveer a los tomadores de decisiones con información actualizada.


Subject(s)
COVID-19/therapy , Delivery of Health Care/statistics & numerical data , Health Resources/statistics & numerical data , Models, Statistical , COVID-19/epidemiology , Colombia , Health Resources/supply & distribution , Hospital Bed Capacity/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data
15.
Acta Anaesthesiol Scand ; 65(6): 755-760, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1096648

ABSTRACT

BACKGROUND: The initial wave of the Covid-19 pandemic has hit Italy, and Lombardy in particular, with violence, forcing to reshape all hospitals' activities; this happened even in pediatric hospitals, although the young population seemed initially spared from the disease. "Vittore Buzzi" Children's Hospital, which is a pediatric/maternal hospital located in Milan (Lombardy Region), had to stop elective procedures-with the exception of urgent/emergent ones-between February and May 2020 to leave space and resources to adults' care. We describe the challenges of reshaping the hospital's identity and structure, and restarting pediatric surgery and anesthesia, from May on, in the most hit area of the world, with the purpose to avoid and contain infections. Both patients and caregivers admitted to hospital have been tested for Sars-CoV-2 in every case. METHODS: Observational cohort study via review of clinical charts of patients undergoing surgery between 16th May and 30th September 2020, together with SARS-CoV -2 RT-PCR testing outcomes, and comparison to same period surgeries in 2019. RESULTS: An increase of approximately 70% in pediatric surgeries (OR 1.68 [1.33-2.13], P < .001) and a higher increase in the number of surgeries were reported (OR 1.75 (1.43-2.15), P < .001). Considering only urgent procedures, a significant difference in the distribution of the type of surgery was observed (Chi-squared P-value < .001). Sars-CoV-2-positive patients have been 0.8% of total number; 14% of these was discovered through caregiver's positivity. CONCLUSION: We describe our pathway for safe pediatric surgery and anesthesia and the importance of testing both patient and caregiver.


Subject(s)
Anesthesia Department, Hospital/organization & administration , Appointments and Schedules , COVID-19 Nucleic Acid Testing , COVID-19/epidemiology , Hospitals, Pediatric/organization & administration , Hospitals, University/organization & administration , Pandemics , SARS-CoV-2 , Surgery Department, Hospital/organization & administration , Surgical Procedures, Operative/statistics & numerical data , Tertiary Care Centers/organization & administration , Adolescent , COVID-19 Nucleic Acid Testing/statistics & numerical data , Caregivers , Child , Child, Preschool , Cohort Studies , Diagnosis-Related Groups , Elective Surgical Procedures/statistics & numerical data , Emergencies/epidemiology , Female , Hospital Bed Capacity/statistics & numerical data , Hospitals, Pediatric/statistics & numerical data , Hospitals, University/statistics & numerical data , Hospitals, Urban/organization & administration , Hospitals, Urban/statistics & numerical data , Humans , Infant , Infant, Newborn , Infection Control/methods , Infectious Disease Transmission, Patient-to-Professional/prevention & control , Italy/epidemiology , Male , Nasopharynx/virology , Patients , SARS-CoV-2/isolation & purification , Symptom Assessment , Tertiary Care Centers/statistics & numerical data , Young Adult
16.
J Biomed Inform ; 116: 103715, 2021 04.
Article in English | MEDLINE | ID: covidwho-1087035

ABSTRACT

Data quality is essential to the success of the most simple and the most complex analysis. In the context of the COVID-19 pandemic, large-scale data sharing across the US and around the world has played an important role in public health responses to the pandemic and has been crucial to understanding and predicting its likely course. In California, hospitals have been required to report a large volume of daily data related to COVID-19. In order to meet this need, electronic health records (EHRs) have played an important role, but the challenges of reporting high-quality data in real-time from EHR data sources have not been explored. We describe some of the challenges of utilizing EHR data for this purpose from the perspective of a large, integrated, mixed-payer health system in northern California, US. We emphasize some of the inadequacies inherent to EHR data using several specific examples, and explore the clinical-analytic gap that forms the basis for some of these inadequacies. We highlight the need for data and analytics to be incorporated into the early stages of clinical crisis planning in order to utilize EHR data to full advantage. We further propose that lessons learned from the COVID-19 pandemic can result in the formation of collaborative teams joining clinical operations, informatics, data analytics, and research, ultimately resulting in improved data quality to support effective crisis response.


Subject(s)
COVID-19/epidemiology , Electronic Health Records , Pandemics , SARS-CoV-2 , COVID-19/mortality , COVID-19/therapy , California/epidemiology , Data Accuracy , Delivery of Health Care, Integrated/statistics & numerical data , Electronic Health Records/statistics & numerical data , Health Information Exchange/statistics & numerical data , Hospital Bed Capacity/statistics & numerical data , Humans , Information Dissemination/methods , Medical Informatics , Pandemics/statistics & numerical data
18.
Anaesth Crit Care Pain Med ; 39(6): 709-715, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-1059695

ABSTRACT

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.


Subject(s)
COVID-19/epidemiology , Hospital Bed Capacity/statistics & numerical data , Intensive Care Units/statistics & numerical data , National Health Programs , Pandemics , SARS-CoV-2 , Bed Conversion/statistics & numerical data , France/epidemiology , Health Care Surveys/statistics & numerical data , Hospitalization/statistics & numerical data , Humans , Personnel Staffing and Scheduling/statistics & numerical data , Personnel, Hospital/supply & distribution , Retrospective Studies , Ventilators, Mechanical/supply & distribution
19.
J Hosp Med ; 16(4): 211-214, 2021 04.
Article in English | MEDLINE | ID: covidwho-1049205

ABSTRACT

Although the impact of COVID-19 has varied greatly across the United States, there has been little assessment of hospital resources and mortality. We examine hospital resources and death counts among hospital referral regions from March 1 to July 26, 2020. This was an analysis of American Hospital Association data with COVID-19 data from the New York Times. Hospital-based resource availabilities were characterized per COVID-19 case. Death count was defined by monthly confirmed COVID-19 deaths. Geographic areas with fewer intensive care unit beds (incident rate ratio [IRR], 0.194; 95% CI, 0.076-0.491), nurses (IRR, 0.927; 95% CI, 0.888-0.967), and general medicine/surgical beds (IRR, 0.800; 95% CI, 0.696-0.920) per COVID-19 case were statistically significantly associated with an increased incidence rate of death in April 2020. This underscores the potential impact of innovative hospital capacity protocols and care models to create resource flexibility to limit system overload early in a pandemic.


Subject(s)
COVID-19/mortality , Health Resources/supply & distribution , Hospital Mortality/trends , Intensive Care Units/statistics & numerical data , Health Personnel/statistics & numerical data , Hospital Bed Capacity/statistics & numerical data , Hospitals , Humans , Incidence , United States
20.
Tunis Med ; 98(10): 657-663, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-1040299

ABSTRACT

OBJECTIVE: To compile the lessons learned in the Greater Maghreb, during the first six months of the fight against the COVID-19 pandemic, in the field of "capacity building" of community resilience. METHODS: An expert consultation was conducted during the first week of May 2020, using the "Delphi" technique. An email was sent requesting the formulation of a lesson, in the form of a "Public Health" good practice recommendation. The final text of the lessons was finalized by the group coordinator and validated by the signatories of the manuscript. RESULTS: A list of five lessons of resilience has been deduced and approved : 1. Elaboration of "white plans" for epidemic management; 2. Training in epidemic management; 3. Uniqueness of the health system command; 4. Mobilization of retirees and volunteers; 5. Revision of the map sanitary. CONCLUSION: Based on the evaluation of the performance of the Maghreb fight against COVID-19, characterized by low resilience, this list of lessons could constitute a roadmap for the reform of Maghreb health systems, towards more performance to manage possible waves of COVID-19 or new emerging diseases with epidemic tendency.


Subject(s)
COVID-19/epidemiology , COVID-19/therapy , Delivery of Health Care/organization & administration , Delivery of Health Care/standards , Health Care Reform , Africa, Northern/epidemiology , Algeria/epidemiology , Attitude of Health Personnel , Civil Defense/methods , Civil Defense/organization & administration , Civil Defense/standards , Community Participation/methods , Conflict of Interest , Delivery of Health Care/statistics & numerical data , Delphi Technique , Expert Testimony , Global Health/standards , Health Care Reform/organization & administration , Health Care Reform/standards , Hospital Bed Capacity/standards , Hospital Bed Capacity/statistics & numerical data , Humans , Mauritania/epidemiology , National Health Programs/organization & administration , National Health Programs/standards , Pandemics , Public Health/methods , Public Health/standards , SARS-CoV-2/physiology , Tunisia/epidemiology
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