scholarly journals SP4.2.11 Access to emergency theatre during the COVID -19 pandemic

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Alexios Dosis ◽  
Jie Qi Lim ◽  
Dharsshini Reveendran ◽  
Kiara Paramjothy ◽  
Sonia Lockwood
Keyword(s):  
Statistical Analysis ◽  
Surgical Procedures ◽  
Waiting Time ◽  
Waiting Times ◽  
P Value ◽  
Surgical Emergencies ◽  
Time Periods ◽  
Spss Software ◽  
The Impact ◽  
Second Peak

Abstract Aims To investigate the impact of the COVID-19 pandemic on general surgical emergencies and access to theatre during the pandemic. Methods We retrospectively reviewed emergency theatre lists in three distinct time periods: October 2019 (pre-COVID-19 era), April 2020 (first peak) and October 2020 (regional second peak). We extracted and compared data from a prospectively maintained database to calculate patient waiting times. Statistical analysis was performed with SPSS software v21.0 to compare median waiting times between groups and significance was set to a p value of < 0.05. Results Conclusions Despite the initial major drop in general surgical procedures and waiting times, the decreased availability of theatre lists due to staff redeployment and sickness, the introduction of routine pre-operative COVID-19 testing have all resulted in a significant increase in waiting time for urgent (CEPOD 2A) cases during the second peak.

scholarly journals Pattern analysis of S. haematobium infection and elimination in the area of Alsuki, Sudan

2021 ◽  
Vol 8 (3) ◽  
pp. 1059
Author(s):  
Rawia A. Abdelshafie ◽  
Abdalla I. Abdalla Mohamed
Keyword(s):  
Risk Factors ◽  
Statistical Analysis ◽  
Health Program ◽  
Pattern Analysis ◽  
Cross Sectional Study ◽  
P Value ◽  
Sectional Study ◽  
Cross Sectional ◽  
The People ◽  
The Impact

Background: The aim of this study was to determine the current conditions of children of the selected area, for the purpose of measuring the new effective health program for schistosomiasis disease eliminations and obtain the prevalence intensity and risk factors of S. haematobium among school children in the study area.Methods: A descriptive cross-sectional study was used to screen school going children of all the ages from five randomly selected schools from Alsuki region. A statistical analysis derived from data formulated based on 1062 samples aged between 6 and 15 years attending the selected schools during the period testing within 6 months were enrolled.Results: The impact of health awareness program was measured usefully and the responded factor for reducing the Schistosomiasis diseases was significantly became less than (0.05). Therefore, the actual qualified fitting degree and applicability was significantly becoming (p value=0.001).Conclusions: This research concluded that the prediction of Schistosomiasis diseases due to the risk Ratio of the collected data for those who did not attended awareness over the people who attended program became (0.248).

A Measure to Detect Sleep Onset Using Statistical Analysis of Spike Rhythmicity

2014 ◽  
Vol 3 (1) ◽  
pp. 27-41 ◽  
Author(s):  
B.R. Purnima ◽  
N. Sriraam ◽  
U. Krishnaswamy ◽  
K. Radhika
Keyword(s):  
Statistical Analysis ◽  
Sleep Onset ◽  
Rank Test ◽  
P Value ◽  
Sleep State ◽  
Statistical Measures ◽  
Plot Analysis ◽  
Significant Difference ◽  
Signed Rank Test ◽  
The Impact

Electroencephalogram (EEG) signals derived from polysomnography recordings play an important role in assessing the physiological and behavioral changes during onset of sleep. This paper suggests a spike rhythmicity based feature for discriminating the wake and sleep state. The polysomnography recordings are segmented into 1 second EEG patterns to ensure stationarity of the signal and four windowing scheme overlaps (0%, 50%, 60% and 75%)of EEG pattern are introduced to study the influence of the pre-processing procedure. The application of spike rhythmicity feature helps to estimate the number of spikes from the given pattern with a threshold of 25%.Then non parametric statistical analysis using Wilcoxon signed rank test is introduced to evaluate the impact of statistical measures such as mean, standard deviation, p-value and box-plot analysis under various conditions .The statistical test shows significant difference between wake and sleep with p<0.005 for the applied feature, thus demonstrating the efficiency of simple thresholding in distinguishing sleep and wake stage .

Ferry Route Level of Service

1997 ◽  
Vol 1608 (1) ◽  
pp. 6-16 ◽  
Author(s):  
Joseph P. Savage
Keyword(s):  
Statistical Analysis ◽  
Waiting Time ◽  
Waiting Times ◽  
Absolute Number ◽  
Service Level ◽  
Washington State ◽  
Point Of View ◽  
Level Of Service ◽  
Transportation Service

Transportation service level measurements have been commonly used and accepted for highway systems, but similar service measures for ferry systems are less common, especially from the users’ point of view. An approach to measuring ferry route level of service is described that allows comparisons among ferry routes and between ferries and alternate modes such as highways (i.e., drive-around choices) and transit. The recommended approach focuses on excess user waiting times (excess delay) by mode (automobile, registered carpool or vanpool, bus, truck, and walk-on passenger), combined with calibrated relationships between volume-to-capacity (V/C) ratio and user delays for forecasting purposes. Data on waiting times for vehicles in the queues were collected on all ferry routes serviced by Washington State Ferries, and an extensive statistical analysis was performed to compute the relationships between V/C ratios and excess waiting times. Excess delay was defined as the waiting time for missed vessel sailings due to overloads, if any, after a ferry patron has arrived at the dock. User delays were expressed in two forms: absolute number of minutes of waiting time, and the number of boat sailings missed before boarding a ferry. The “boat wait” concept was introduced to differentiate between excess delays caused by congestion that prevents a driver from boarding the next ferry, and delays related to the amount of service provided on a route as reflected in the headways between vessels.

scholarly journals High emergency organ allocation rule in lung transplantation: a simulation study

2017 ◽  
Vol 3 (4) ◽  
pp. 00020-2017 ◽  
Author(s):  
Julien Riou ◽  
Pierre-Yves Boëlle ◽  
Jason D. Christie ◽  
Gabriel Thabut
Keyword(s):  
Lung Transplantation ◽  
Waiting Time ◽  
Waiting Times ◽  
Organ Allocation ◽  
Waiting List ◽  
Allocation Rule ◽  
Long Term Effects ◽  
Term Survival ◽  
The Impact

The scarcity of suitable organ donors leads to protracted waiting times and mortality in patients awaiting lung transplantation. This study aims to assess the short- and long-term effects of a high emergency organ allocation policy on the outcome of lung transplantation.We developed a simulation model of lung transplantation waiting queues under two allocation strategies, based either on waiting time only or on additional criteria to prioritise the sickest patients. The model was informed by data from the United Network for Organ Sharing. We compared the impact of these strategies on waiting time, waiting list mortality and overall survival in various situations of organ scarcity.The impact of a high emergency allocation strategy depends largely on the organ supply. When organ supply is sufficient (>95 organs per 100 patients), it may prevent a small number of early deaths (1 year survival: 93.7% against 92.4% for waiting time only) without significant impact on waiting times or long-term survival. When the organ/recipient ratio is lower, the benefits in early mortality are larger but are counterbalanced by a dramatic increase of the size of the waiting list. Consequently, we observed a progressive increase of mortality on the waiting list (although still lower than with waiting time only), a deterioration of patients’ condition at transplant and a decrease of post-transplant survival times.High emergency organ allocation is an effective strategy to reduce mortality on the waiting list, but causes a disruption of the list equilibrium that may have detrimental long-term effects in situations of significant organ scarcity.

scholarly journals EP.WE.844Two-week bowel cancer referrals during the COVID pandemic

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Ramez Antakia ◽  
Vladimir Popa-Nimigean ◽  
Thomas Athisayaraj
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Bowel Habit ◽  
Red Flags ◽  
National Guidelines ◽  
Mean Waiting Time ◽  
Significant Difference ◽  
Immunochemical Test ◽  
Red Flag ◽  
The Impact

Abstract Aims The aims were to assess the impact of the COVID-19 pandemic on the waiting times for patients referred via the two-week pathway for suspected colorectal cancer. We also examined the use of Faecal Immunochemical Test (FIT) alongside the presenting complaints in triaging/prioritising patients for further imaging and/or endoscopic investigations appropriately. Methods A list of all patients referred via the two-week pathway to the West Suffolk Hospital for suspected colorectal cancers from 30/01/2020 to 19/07/2020 was compiled. The main four red flag symptoms were change in bowel habit (CIBH), anorectal bleeding, anaemia and weight loss. A subset of 235 patients were closely examined regarding their presenting complaints, FIT, imaging and endoscopy results with analysis of outcomes. Results 127 male versus 108 female patients were included. 59.61% of patients who were eligible for the FIT test received one. Mean waiting time for FIT positive patients was 42.39 (95% CI) versus 61.10 (95% CI) for FIT negative patients. Patients with one or two red flags symptoms had a mean waiting time of 44.81 days (95% CI 35.79-53.82) and 47.91 days (95% CI 38.07-57.75) respectively. Patients with three red flag symptoms had a mean waiting time of 28.2 days (95% CI 17.94-38.39). There was a statistically significant difference in mean waiting time between patients having 1-2 symptoms and patients with three symptoms (p &lt; 0.005). Conclusions Despite delays during the COVID pandemic particularly for endoscopy, high risk and FIT positive patients were prioritised. Waiting times were still higher than advised national guidelines.

How Should Lungs Be Allocated for Transplant?

2018 ◽  
Vol 39 (02) ◽  
pp. 126-137 ◽  
Author(s):  
Thomas Egan
Keyword(s):  
Waiting Time ◽  
Organ Procurement ◽  
Waiting Times ◽  
Organ Transplant ◽  
Health Resources ◽  
Organ Allocation ◽  
Final Rule ◽  
History Of ◽  
End Stage ◽  
The Impact

AbstractAs lung transplantation became established therapy for end-stage lung disease, there were not nearly enough suitable lungs from brain-dead organ donors to meet the need, leading to a focus on how lungs are allocated for transplant. Originally lungs were allocated by the United Network for Organ Sharing (UNOS) like hearts—by waiting time, first to listed recipients in the organ procurement organization of the donor, then to potential recipients in concentric 500 nautical mile circles. This resulted in long waiting times and increasing waitlist deaths. In 1999, the Health Resources and Services Administration published a Final Rule, requesting UNOS to review organ allocation algorithms to ensure that they complied with the desire to allocate organs based on urgency, avoiding futile transplants, and minimizing the role of waiting time in organ allocation. This led to development of the lung allocation score (LAS), which allocates lungs based on urgency and transplant benefit, introduced in 2005. The U.S. LAS system was adopted by Eurotransplant to allocate unused lungs between donor countries, and by both Germany and the Netherlands for lung allocation in their countries. This article will review the history of lung allocation, discuss the impact of LAS and its shortcomings, suggest recommendations to increase the number of lungs for transplant, and improve allocation of donated lungs. Ultimately, the goal of organ transplant research is to have so many organs to transplant that allocation systems are unnecessary.

A Measure to Detect Sleep Onset Using Statistical Analysis of Spike Rhythmicity

2015 ◽  
pp. 1567-1578
Author(s):  
B.R. Purnima ◽  
N. Sriraam ◽  
U. Krishnaswamy ◽  
K. Radhika
Keyword(s):  
Statistical Analysis ◽  
Sleep Onset ◽  
Rank Test ◽  
P Value ◽  
Sleep State ◽  
Statistical Measures ◽  
Plot Analysis ◽  
Signed Rank ◽  
Signed Rank Test ◽  
The Impact

Electroencephalogram (EEG) signals derived from polysomnography recordings play an important role in assessing the physiological and behavioral changes during onset of sleep. This paper suggests a spike rhythmicity based feature for discriminating the wake and sleep state. The polysomnography recordings are segmented into 1 second EEG patterns to ensure stationarity of the signal and four windowing scheme overlaps (0%, 50%, 60% and 75%) of EEG pattern are introduced to study the influence of the pre-processing procedure. The application of spike rhythmicity feature helps to estimate the number of spikes from the given pattern with a threshold of 25%.Then non parametric statistical analysis using Wilcoxon signed rank test is introduced to evaluate the impact of statistical measures such as mean, standard deviation, p-value and box-plot analysis under various conditions .The statistical test shows significant difference between wake and sleep with p<0.005 for the applied feature, thus demonstrating the efficiency of simple thresholding in distinguishing sleep and wake stage .

scholarly journals The Clinical Nursing and Midwifery Dashboard (CNMD): A State-Wide Implementation

2021 ◽  
Author(s):  
Ahmad Abdel-Hafez ◽  
Don Baker ◽  
Michelle Winning ◽  
Alan Scanlon
Keyword(s):  
Statistical Analysis ◽  
Risk Assessments ◽  
P Value ◽  
Maternity Unit ◽  
Evaluation Approach ◽  
Clinical Nursing ◽  
Real Time Information ◽  
The Impact ◽  
Time Information ◽  
Completion Times

The clinical nursing and midwifery dashboard (CNMD) was built to provide a near real-time information and data visualisations for nurse unit managers (NUMs) and maternity unit managers (MUMs) within only a 5-15 minutes delay from when they enter data to the integrated electronic medical records (ieMR) system. The dashboard displays metrics and information about current adult inpatients in overnight wards. The aim is to support NUMs and MUMs to manage their daily workload and have continuous visibility of patients nursing risk and safety assessment documentation. A quantitative evaluation approach was conducted to measure the impact of the dashboard on key performance indicators. Statistical analysis was completed to compare risk assessment average completion times prior to and post CNMD implementation. The results of the evaluation were positive, and the statistical analysis shows significant reduction in the average time to complete different risk assessments with p-value<0.01.

scholarly journals Association of patients with fluorosis who underwent vital bleaching - A hospital-based study

2020 ◽  
Vol 11 (SPL3) ◽  
pp. 1592-1597
Author(s):  
Sadhvi B ◽  
Delphine priscilla Antony S ◽  
Suresh V
Keyword(s):  
Statistical Analysis ◽  
Data Collection ◽  
Low Cost ◽  
Descriptive Study ◽  
Tooth Bleaching ◽  
Permanent Teeth ◽  
P Value ◽  
Treatment Approaches ◽  
Spss Software

Fluorosis staining is an esthetic problem, especially when in anterior. Numerous treatment approaches include Conservative approaches such as vital tooth bleaching, which is the most sought option to eliminate fluorosis staining as it is simple, low cost, noninvasive. Further, it is relatively rapid, no distortion of enamel and can be safely used for permanent teeth. This research emphasizes the association of patients with fluorosis who underwent vital bleaching to correct the staining and to obtain esthetically pleasing teeth. This is a comparative and descriptive study for which the data was obtained from DIAS (dental information archiving software). The data collection was from the Undergraduate and postgraduate clinics, Saveetha dental college, SIMATS. The data was collected and compiled, followed by its statistical analysis by using the SPSS software by IBM. Results revealed the incidence of Vital bleaching for fluorosis was 76.7%, and vital bleaching for other reasons was 23.3%; Out of the patients who underwent vital bleaching for fluorosis, males were 86.7%, and females were 13.3% with P-value < 0.05 statistically significant; patients from 1-34 years were 83.3%, 35-55 years were 10.0% and above 55 years were 6.7% with P-value < 0.05 statistically significant. The overall results show a Male predilection in vital bleaching for fluorosis and 1-34 years as the most predominant population for undergoing vital bleaching among fluorosis patients.

scholarly journals P042 Five-year review of a tertiary paediatric sleep service for complex patients

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A35-A35
Author(s):  
A Griffiths ◽  
S Preston ◽  
A Adams ◽  
M Vandeleur
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Diagnostic Study ◽  
Sleep Study ◽  
Clinical Model ◽  
Home Study ◽  
Sleep Studies ◽  
Mean Waiting Time ◽  
Bed Capacity ◽  
The Impact

Abstract Introduction Our paediatric sleep unit commenced service for children with complex medical problems in July 2015. Service capacity includes 12 inpatient level 1 studies (two neonates) and one home study per week. FTE includes senior scientists 2.6, sleep technologists 1.7, administration 1.0, nursing 0.7 and medical 1.2. The primary aim of this study was to evaluate activity during the first 5-years. The secondary aim was to document the impact of the COVID-19 pandemic. Methods Sleep unit operational & diagnostic data were collected from sleep booking sheets, sleep study reports, electronic medical records. Descriptive statistics are presented. Results A total of 2186 sleep studies were performed (July 2015 to June 2020) with a range of 368–472 studies per annum. Overall, 61.7% were diagnostic studies, 20.8% titration studies (CPAP, oxygen, bi-level or invasive ventilation), 10% neonatal and 7.5% home studies. Between 2016–2020, the average waiting time (days) for a neonatal study was 16, a titration study was 106, a diagnostic study was 110 and a home study was 76. Further delays were caused by the COVID19 pandemic. Mean waiting time rose 229% from 108 days (Feb 2020) to 355 days (Feb 2021). Referrals for sleep studies have exceeded bed capacity since the beginning of the pandemic. Discussion This audit describes activity in a tertiary complex paediatric sleep service during the first 5 years. The service has struggled on current FTE and bed capacity to manage waiting times, exacerbated further by the COVID-19 pandemic. A new business and clinical model are warranted.

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