Risk assessment and rationalization of health resource allocation: Lessons from the Brazilian COVID-19 cohort in 2020.
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ABSTRACT:
SUBMITTER: Cattani VB
PROVIDER: S-EPMC8809658 | biostudies-literature | 2022 Apr
REPOSITORIES: biostudies-literature
Publications
Risk assessment and rationalization of health resource allocation: Lessons from the Brazilian COVID-19 cohort in 2020.
Cattani Vitória Berg VB Dos Santos Thaís Araujo TA Castro-Alves Julio J Ribeiro-Alves Marcelo M
Preventive medicine reports 20220202
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Project description: Not available
| S-BSST563 | biostudies-other
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2023-03-15 | E-MTAB-12777 | biostudies-arrayexpress
Project description:In order to identify differentially abundant proteins, human plasma samples from COVID-19 patients with either a mild or moderate (MM) or a critical or severe (CS) disease course from the acute phases of infection were analyzed on antibody microarrays targeting 351 different proteins by 517 antibodies.
2023-04-11 | E-MTAB-12779 | biostudies-arrayexpress
Project description:In an era where conditions for education are rapidly changing globally, online assessment presents several opportunities as well as challenges in the higher education landscape. The forceful transition from face-to-face to online assessments, as part of the emergency implementation of online learning due to the COVID-19 pandemic, has affected teaching, learning, and assessment experiences worldwide. This study explores how faculty members in Saudi universities secured their online assessment during phase one of the COVID-19 pandemic. The research aims were: 1) identifying faculty assessment practices before the onset of COVID-19 and comparing these with practices during the pandemic, and 2) identifying the major challenges faced by the faculty members of the study in securing their online assessment to ensure that academic integrity and assessment standards remained intact. Data were collected from seven university professors through a self-reporting survey, followed by semi-structured interviews. The faculty members found the pandemic period to be the best time to change their assessment methods, and incorporate innovative ideas that conformed to both their own beliefs and students’ needs. The factors that influenced the faculty’s assessment alterations were their personal beliefs and learnings from others’ experiences, in addition to the guidelines issued by Saudi Arabia’s Ministry of Education. The results of this study have implications for the development of post-COVID-19 assessment practices and professional development priorities. Supplementary Information The online version contains supplementary material available at 10.1007/s40979-022-00112-1.
| S-EPMC9330967 | biostudies-literature
Project description:To face the COVID-19 pandemic, prophylactic vaccines have been developed in record time, but vaccine coverage is still limited, accessibility is not equitable worldwide, and the vaccines are not fully effective against emerging variants. Therefore, therapeutic treatments are urgently needed to control the pandemic and treat vulnerable populations, but despite all efforts made, options remain scarce. However, the knowledge gained during 2020 constitutes an invaluable platform from which to build future therapies. In this review, we highlight the main drug repurposing strategies and achievements made over the first 18 months of the pandemic, but also discuss the antivirals, immunomodulators and drug combinations that could be used in the near future to cure COVID-19.
| S-EPMC8598952 | biostudies-literature
Project description:Background: The current pandemic requires hospitals to ensure care not only for the growing number of COVID-19 patients but also regular patients. Hospital resources must be allocated accordingly. Objective: To provide hospitals with a planning model to optimally allocate resources to intensive care units given a certain incidence of COVID-19 cases. Methods: The analysis included 334 cases from four adjacent counties south-west of Munich. From length of stay and type of ward [general ward (NOR), intensive care unit (ICU)] probabilities of case numbers within a hospital at a certain time point were derived. The epidemiological situation was simulated by the effective reproduction number R, the infection rates in mid-August 2020 in the counties, and the German hospitalization rate. Simulation results are compared with real data from 2nd and 3rd wave (September 2020-May 2021). Results: With R = 2, a hospitalization rate of 17%, mitigation measures implemented on day 9 (i.e., 7-day incidence surpassing 50/100,000), the peak occupancy was reached on day 22 (155.1 beds) for the normal ward and on day 25 (44.9 beds) for the intensive care unit. A higher R led to higher occupancy rates. Simulated number of infections and intensive care unit occupancy was concordant in validation with real data obtained from the 2nd and 3rd waves in Germany. Conclusion: Hospitals could expect a peak occupancy of normal ward and intensive care unit within ~5-11 days after infections reached their peak and critical resources could be allocated accordingly. This delay (in particular for the peak of intensive care unit occupancy) might give options for timely preparation of additional intensive care unit resources.
| S-EPMC10012637 | biostudies-literature
Project description:BackgroundA lack of health resources is a common problem after the outbreak of infectious diseases, and resource optimization is an important means to solve the lack of prevention and control capacity caused by resource constraints. This study systematically evaluated the similarities and differences in the application of coronavirus disease (COVID-19) resource allocation models and analyzed the effects of different optimal resource allocations on epidemic control.MethodsA systematic literature search was conducted of CNKI, WanFang, VIP, CBD, PubMed, Web of Science, Scopus and Embase for articles published from January 1, 2019, through November 23, 2023. Two reviewers independently evaluated the quality of the included studies, extracted and cross-checked the data. Moreover, publication bias and sensitivity analysis were evaluated.ResultsA total of 22 articles were included for systematic review; in the application of optimal allocation models, 59.09% of the studies used propagation dynamics models to simulate the allocation of various resources, and some scholars also used mathematical optimization functions (36.36%) and machine learning algorithms (31.82%) to solve the problem of resource allocation; the results of the systematic review show that differential equation modeling was more considered when testing resources optimization, the optimization function or machine learning algorithm were mostly used to optimize the bed resources; the meta-analysis results showed that the epidemic trend was obviously effectively controlled through the optimal allocation of resources, and the average control efficiency was 0.38(95%CI 0.25-0.51); Subgroup analysis revealed that the average control efficiency from high to low was health specialists 0.48(95%CI 0.37-0.59), vaccines 0.47(95%CI 0.11-0.82), testing 0.38(95%CI 0.19-0.57), personal protective equipment (PPE) 0.38(95%CI 0.06-0.70), beds 0.34(95%CI 0.14-0.53), medicines and equipment for treatment 0.32(95%CI 0.12-0.51); Funnel plots and Egger's test showed no publication bias, and sensitivity analysis suggested robust results.ConclusionWhen the data are insufficient and the simulation time is short, the researchers mostly use the constructor for research; When the data are relatively sufficient and the simulation time is long, researchers choose differential equations or machine learning algorithms for research. In addition, our study showed that control efficiency is an important indicator to evaluate the effectiveness of epidemic prevention and control. Through the optimization of medical staff and vaccine allocation, greater prevention and control effects can be achieved.
| S-EPMC10865525 | biostudies-literature
Project description:BackgroundThe coronavirus disease 2019 (COVID-19) pandemic challenged global infrastructure. Healthcare systems were forced to reallocate resources toward the frontlines. In this systematic review, we analyze the impact of resource reallocation during the COVID-19 pandemic on the diagnosis, management, and outcomes of esophageal cancer (EC) patients.MethodsPubMed and Embase were systematically searched for articles investigating the impact of the COVID-19 pandemic on EC patients. Of the 1,722 manuscripts initially screened, 23 met the inclusion criteria.ResultsHeterogeneity of data and outcomes reporting prohibited aggregate analysis. Reduced detection of EC and considerable variability in disease stage at presentation were noted during the COVID-19 pandemic. EC patients experienced delays in diagnostic and preoperative staging investigations but surgical resection was not associated with greater short-term morbidity or mortality. Modeling the impact of pandemic-related delays in EC care predicts significant reductions in survival with associated economic losses in the coming years.ConclusionsAmidst resource scarcity during the COVID-19 pandemic, the multidisciplinary management of patients with EC was affected at multiple stages in the care pathway. Although the complete ramifications of reductions in EC diagnosis and delays in care remain unclear, EC surgery was able to safely continue as a result of collaboration between centers, strict adherence to COVID-19 protective measures, and reallocation of healthcare resources towards the same. Ultimately, when healthcare systems are pushed to the brink, the downstream consequences of resource reallocation require judicious analysis to optimize overall patient outcomes.
| S-EPMC10944786 | biostudies-literature
Project description:ImportanceThe spring 2020 surge of COVID-19 unprecedentedly strained ventilator supply in New York City, with many hospitals nearly exhausting available ventilators and subsequently seriously considering enacting crisis standards of care and implementing New York State Ventilator Allocation Guidelines (NYVAG). However, there is little evidence as to how NYVAG would perform if implemented.ObjectivesTo evaluate the performance and potential improvement of NYVAG during a surge of patients with respect to the length of rationing, overall mortality, and worsening health disparities.Design, setting, and participantsThis cohort study included intubated patients in a single health system in New York City from March through July 2020. A total of 20 000 simulations were conducted of ventilator triage (10 000 following NYVAG and 10 000 following a proposed improved NYVAG) during a crisis period, defined as the point at which the prepandemic ventilator supply was 95% utilized.ExposuresThe NYVAG protocol for triage ventilators.Main outcomes and measuresComparison of observed survival rates with simulations of scenarios requiring NYVAG ventilator rationing.ResultsThe total cohort included 1671 patients; of these, 674 intubated patients (mean [SD] age, 63.7 [13.8] years; 465 male [69.9%]) were included in the crisis period, with 571 (84.7%) testing positive for COVID-19. Simulated ventilator rationing occurred for 163.9 patients over 15.0 days, 44.4% (95% CI, 38.3%-50.0%) of whom would have survived if provided a ventilator while only 34.8% (95% CI, 28.5%-40.0%) of those newly intubated patients receiving a reallocated ventilator survived. While triage categorization at the time of intubation exhibited partial prognostic differentiation, 94.8% of all ventilator rationing occurred after a time trial. Within this subset, 43.1% were intubated for 7 or more days with a favorable SOFA score that had not improved. An estimated 60.6% of these patients would have survived if sustained on a ventilator. Revising triage subcategorization, proposed improved NYVAG, would have improved this alarming ventilator allocation inefficiency (25.3% [95% CI, 22.1%-28.4%] of those selected for ventilator rationing would have survived if provided a ventilator). NYVAG ventilator rationing did not exacerbate existing health disparities.Conclusions and relevanceIn this cohort study of intubated patients experiencing simulated ventilator rationing during the apex of the New York City COVID-19 2020 surge, NYVAG diverted ventilators from patients with a higher chance of survival to those with a lower chance of survival. Future efforts should be focused on triage subcategorization, which improved this triage inefficiency, and ventilator rationing after a time trial, when most ventilator rationing occurred.
| S-EPMC10556967 | biostudies-literature
Project description:ObjectiveExtracorporeal membrane oxygenation (ECMO) resource allocation tools are currently lacking. We developed machine learning (ML) models for predicting COVID-19 patients at risk of receiving ECMO to guide patient triage and resource allocation.Material and methodsWe included COVID-19 patients admitted to intensive care units for >24 h from March 2020 to October 2021, divided into training and testing development and testing-only holdout cohorts. We developed ECMO deployment timely prediction model ForecastECMO using Gradient Boosting Tree (GBT), with pre-ECMO prediction horizons from 0 to 48 h, compared to PaO2/FiO2 ratio, Sequential Organ Failure Assessment score, PREdiction of Survival on ECMO Therapy score, logistic regression, and 30 pre-selected clinical variables GBT Clinical GBT models, with area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics.ResultsECMO prevalence was 2.89% and 1.73% in development and holdout cohorts. ForecastECMO had the best performance in both cohorts. At the 18-h prediction horizon, a potentially clinically actionable pre-ECMO window, ForecastECMO, had the highest AUROC (0.94 and 0.95) and AUPRC (0.54 and 0.37) in development and holdout cohorts in identifying ECMO patients without data 18 h prior to ECMO.Discussion and conclusionsWe developed a multi-horizon model, ForecastECMO, with high performance in identifying patients receiving ECMO at various prediction horizons. This model has potential to be used as early alert tool to guide ECMO resource allocation for COVID-19 patients. Future prospective multicenter validation would provide evidence for generalizability and real-world application of such models to improve patient outcomes.
| S-EPMC10018267 | biostudies-literature