Determinants of neonatal near misses among neonates admitted to Guji and Borena zones selected public hospitals, Southern Ethiopia, 2021: A facility based unmatched case control study design

There is little available evidence that quantifies the determinats of NNM in Ethiopia despite an increasing magnitude of neonatal mortality. Therefore, this study was designed to provide concrte evidence about the determinats of NNMS among neonates admitted to Guji and Borena Zones Public Hospitals, Southern Ethiopia, 2021. A facility based unmatched case control study design was conducted on 402 (134 cases and 268 controls) selected neonates admitted to Bule Hora, Adola and Yabelo General Hospitals from February 1-March 31, 2021. Cases were consecutively selected. Whereas for each case, two controls were selected by systematic random sampling technique. The data collection included a pretested and structured face-to-face interviewer administered questionnaire with a supplementation of maternal and neonatal medical records with checklists. Then the data were coded and entered in to Epi data version 3.1 and then exported to the Statistical Package for Social Science IBM version 25 for analysis. The descriptive statistics run and the results of the data were presented using frequencies, and tables. Bivariable and multi variable logistic regression was used for the analsysis of the data. Finally, Adjusted Odds Ratio together with 95% Confidence Intervals and p value <0.05 was used to declare the significance of all statistic. A total of 134 cases (neonatal near misses) and 268 controls (normal neonate) were participated in this study to make a response rate of 100% for both cases, and controls. In this study rural residence (AOR = 0.51, 95% CI: 0.27, 0.96), previous history of neonatal death (AOR = 4.85, 95%CI: 2.24,10.49), birth interval ≤ 2 years (AOR = 1.83, 95% CI: 1.04, 3.11) and history of abortion (both induced and miscarriage) (AOR = 1.97, 95%CI: 1.17, 3.31) were found to be statistically significant at a p-value of <0.05. History of prior abortion history of prior neonatal death and short birth interval (≤ 2 years) were identified to be the determinats of NNMs. High quality antenatal and intrapartum continuum of care should be provided for women and neonates. Additionally, contraceptive utilization should be encouraged for a women to space the births of their children.

Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification

Accidents of various types in the construction of hydropower engineering projects occur frequently, which leads to significant numbers of casualties and economic losses. Identifying and eliminating near misses are a significant means of preventing accidents. Mining near-miss data can provide valuable information on how to mitigate and control hazards. However, most of the data generated in the construction of hydropower engineering projects are semi-structured text data without unified standard expression, so data association analysis is time-consuming and labor-intensive. Thus, an artificial intelligence (AI) automatic classification method based on a convolutional neural network (CNN) is adopted to obtain structured data on near-miss locations and near-miss types from safety records. The apriori algorithm is used to further mine the associations between “locations” and “types” by scanning structured data. The association results are visualized using a network diagram. A Sankey diagram is used to reveal the information flow of near-miss specific objects using the “location ⟶ type” strong association rule. The proposed method combines text classification, association rules, and the Sankey diagrams and provides a novel approach for mining semi-structured text. Moreover, the method is proven to be useful and efficient for exploring near-miss distribution laws in hydropower engineering construction to reduce the possibility of accidents and efficiently improve the safety level of hydropower engineering construction sites.

Proposal for Preventing Motor Vehicle Accident Related to Drowsy Driving in Patients With Narcolepsy

Narcolepsy is a chronic sleep disorder characterized by irresistible sleep attacks, hypersomnolence, cataplexy (sudden loss of muscle tone provoked by emotion), and sleep paralysis. Individuals with narcolepsy are at a high risk of experiencing sleepiness while driving leading to road traffic accidents. To prevent such accidents, some countries have regulations for commercial and noncommercial drivers with narcolepsy. Evaluating sleepiness is essential. Therefore, several subjective reports and objective tests were used to predict the possibility of car crashes or near-misses. Brain stimulants are effective in treating narcolepsy and can reduce daytime sleepiness in these patients. However, no guideline has been established for the driving safety of patients with narcolepsy in Korea. The Korean Sleep Research Society has prepared this proposal for preventing motor vehicle accidents caused by drowsy driving in patients with narcolepsy.

Pocus as a tool to avoid diagnostic errors in covid-19 era

X-ray flips and wrong labelling has been uncomfortably common, and often results in wrong side intervention. Wrong side surgery is indeed the most dramatic and visible form of human errors. Until the 1999 Institute of Medicine report ‘To Err is Human’, the medical fraternity was largely unaware of such preventable medical errors and near misses. We herein, describe a case where X-ray flip and mislabelling led to wrong side intervention in a COVID-19 ICU. Active errors in human performance are inevitable while practising medicine in the current COVID-19 pandemic where difficulties in performing comprehensive systemic examination with the protective gear on, long working hours, work stress, emotions, and fatigue interplay with the errors in technology and increase the chances of errors. We propose the use of point of care ultrasound (POCUS) in COVID-19 ICU’s to aid in the diagnosis and management.

Learning from an Incident Reporting System: Developing a Strategy to Prevent Medication Errors Among Nursing Students

Medication errors occur during clinical learning for nursing students. This study aimed to develop learning cases to prevent medication errors using analysis of data from an incident reporting system. This study utilized an action research approach to develop learning cases. These learning cases were implemented with problem-based learning (PBL) method and self-learning materials strategies. The results showed that repeated occurrences of medication errors and near misses were reduced after implementing the new teaching strategy.

Ensuring Health, Safety and Well-Being on the UK’s Thames Tideway Tunnel Programme

Due for completion in 2025, the £4.2 billion Thames Tideway Tunnel programme is one of the largest and most complex civil engineering schemes in the world. This paper reports on how the delivery team ensured the health, safety and well-being of over 4000 people who worked on the programme during the first 6 years of its 9 year delivery. The team collaborated to create a strong safety culture, focusing on excellence in getting the basics right, sharing and adopting best practice, and learning from near misses and incidents. The result has been a very good level of safety performance.

HAZARD IDENTIFICATION AND PROBABLISTIC SCENARIO SELECTION FOR SHIP- SHIP COLLISION ACCIDENTS

In collision risk-based design frameworks it is necessary to accurately define and select a set of credible scenarios to be used in the quantitative assessment and management of the collision risk between two ships. Prescriptive solutions and empirical knowledge are commonly used in current maritime industries, but are often insufficient for innovation because they can result in unfavourable design loads and may not address all circumstances of accidents involved. In this study, an innovative method using probabilistic approaches is proposed to identify relevant groups of ship-ship collision accident scenarios that collectively represent all possible scenarios. Ship-ship collision accidents and near-misses recently occurred worldwide are collated for the period of 21 years during 1991 to 2012. Collision scenarios are then described using a set of parameters that are treated individually as random variables and analysed by statistical methods to define the ranges and variability to formulate the probability density distribution for each scenario. As the consideration of all scenarios would not be practical, a sampling technique is applied to select a certain number of prospective collision scenarios. Applied examples for different types of vessels are presented to demonstrate the applicability of the method.