scholarly journals Systematic media review: A novel method to assess mass-trauma epidemiology in absence of databases—A pilot-study in Rwanda

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258446
Author(s):  
Lotta Velin ◽  
Mbonyintwari Donatien ◽  
Andreas Wladis ◽  
Menelas Nkeshimana ◽  
Robert Riviello ◽  
...  
Keyword(s):  
Natural Hazards ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Median Number ◽  
Mass Casualty ◽  
Road Traffic Accidents ◽  
Eligibility Criteria ◽  
Surge Capacity ◽  
Mass Trauma ◽  
Media Review

Objective Surge capacity refers to preparedness of health systems to face sudden patient inflows, such as mass-casualty incidents (MCI). To strengthen surge capacity, it is essential to understand MCI epidemiology, which is poorly studied in low- and middle-income countries lacking trauma databases. We propose a novel approach, the “systematic media review”, to analyze mass-trauma epidemiology; here piloted in Rwanda. Methods A systematic media review of non-academic publications of MCIs in Rwanda between January 1st, 2010, and September 1st, 2020 was conducted using NexisUni, an academic database for news, business, and legal sources previously used in sociolegal research. All articles identified by the search strategy were screened using eligibility criteria. Data were extracted in a RedCap form and analyzed using descriptive statistics. Findings Of 3187 articles identified, 247 met inclusion criteria. In total, 117 MCIs were described, of which 73 (62.4%) were road-traffic accidents, 23 (19.7%) natural hazards, 20 (17.1%) acts of violence/terrorism, and 1 (0.09%) boat collision. Of Rwanda’s 30 Districts, 29 were affected by mass-trauma, with the rural Western province most frequently affected. Road-traffic accidents was the leading MCI until 2017 when natural hazards became most common. The median number of injured persons per event was 11 (IQR 5–18), and median on-site deaths was 2 (IQR 1–6); with natural hazards having the highest median deaths (6 [IQR 2–18]). Conclusion In Rwanda, MCIs have decreased, although landslides/floods are increasing, preventing a decrease in trauma-related mortality. By training journalists in “mass-casualty reporting”, the potential of the “systematic media review” could be further enhanced, as a way to collect MCI data in settings without databases.

ADHD is a Risk Factor of Road Crashes: A Purposive Study of Finding Relation between ADHD and Road Traffic Accident in Bangladesh

2018 ◽  
Vol 9 (08) ◽  
pp. 20531-20536
Author(s):  
Nusrat Shamima Nur ◽  
M. S. l. Mullick ◽  
Ahmed Hossain
Keyword(s):  
Risk Factor ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Fatality Rate ◽  
Road Traffic Accidents ◽  
Road Crashes ◽  
Road Crash ◽  
Day By Day

Background: In Bangladesh fatality rate due to road traffic accidents is rising sharply day by day. At least 2297 people were killed and 5480 were injured in road traffic accidents within 1st six months of 2017.Whereas in the previous year at 2016 at least 1941 people were killed and 4794 were injured within the 1st six months. No survey has been reported in Bangladesh yet correlating ADHD as a reason of impulsive driving which ends up in a road crash.

Positive Aspects of COVID 19 Pandemic: A Blessing in Disguis

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 187-191
Author(s):  
Anjankar Ashish P ◽  
Anjankar Vaibhav P ◽  
Anjankar Anil J ◽  
Kanyal Lata
Keyword(s):  
Air Pollution ◽  
Heart Attack ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Community Action ◽  
Crime Rates ◽  
Road Traffic Accidents ◽  
Positive Effects ◽  
Positive Side ◽  
The World

COVID 19 is undeniably one of the deadliest diseases that humanity has ever seen. It continues to affect the lives and livelihood of people appallingly across the world. Maximum discussions focus towards the apprehension of catching the infection, dwelling in homes, overpopulated nursing homes and shut down of all kinds. But, here let’s discuss the positive side of COVID 19 pandemic.As COVID 19 has spread its influence all over the world, affected countries have either announced lockdown or have implemented severe restrictions in their respective countries. Because of this, everyone dwells in their homes. Thus, exercising social distancing and functioning from home. All of the above is directed at restricting the transmission of coronavirus and expectantly ostracising the fatality from COVID 19. These transformations have also brought about some unanticipated emanations; some good things have come out of the pandemic as well. Positive effects of COVID 19 are seen on reduced road traffic, and road traffic accidents lowered levels of air pollution which has to lead to lowered heart attack rates and rejuvenating environment. Crime rates have fallen, and expenses are reduced in most places. Community action, communication amongst families, behaviour, sanitation, hygiene, online and distance education has positively impacted by COVID 19 pandemic. COVID 19 despite a bane for humans, can be thought of a boon for living beings. The habitats and elements have been purified with the stringent use of petrochemical products. To breathe fresh air and to consume purified water is a boon by itself. Now, it is time for humans to lead a caring life to every bounty bestowed on them by Nature. This thoughtful and considerate life will give hope for a healthy, stress-free life.

Influence of environmental factors on road traffic accidents: Hospital based cross sectional study at Tirupati

2018 ◽  
Vol 7 (1) ◽  
pp. 52
Author(s):  
Bayapa Reddy N. ◽  
Shakeer Kahn P. ◽  
Surendra Babu D. ◽  
Khadervali N. ◽  
Chandrasekhar C. ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Cross Sectional Study ◽  
Road Traffic Accidents ◽  
Sectional Study ◽  
Cross Sectional

EVALUATION OF THE PATTERNS OF INJURIES IN ROAD TRAFFIC ACCIDENTS IN GREAT CAIRO, EGYPT

2016 ◽  
Vol 16 (2) ◽  
pp. 79-95
Author(s):  
Osama H El Bakash ◽  
Abd El Moty M Kabbash ◽  
Mona SA El Gohary ◽  
Amal SAF Hafez
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Road Traffic Accidents

scholarly journals Comparison of Prediction Models for Mortality Related to Injuries from Road Traffic Accidents after Correcting for Undersampling

2021 ◽  
Vol 18 (11) ◽  
pp. 5604
Author(s):  
Yookyung Boo ◽  
Youngjin Choi
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
National Level ◽  
Brier Score ◽  
Support Vector ◽  
Main Diagnosis ◽  
Road Traffic Accidents ◽  
Multiple Variables ◽  
Type Of Injury ◽  
Kernel Model

In this study, four models—logistic regression (LR), random forest (RF), linear support vector machine (SVM), and radial basis function (RBF)-SVM—were compared for their accuracy in determining mortality caused by road traffic injuries. They were tested using five years of national-level data from the Korea Disease Control and Prevention Agency’s (KDCA) National Hospital Discharge In-Depth Survey (2013 through to 2017). Model performance was measured for accuracy, precision, recall, F1 score, and Brier score metrics using classification analysis that included characteristics of patients, accidents, injuries, and illnesses. Due to the number of variables and differing units, the rates of survival and mortality related to road traffic accidents were imbalanced, so the data was corrected and standardized before the classification models’ performances were compared. Using the importance analysis, the main diagnosis, the type of injury, the site of the injury, the type of injury, the operation status, the type of accident, the role at the time of the accident, and the sex were selected as the analysis factors. The biggest contributing factor was the role in the accident, which is the driver, and the major sites of the injuries were head injuries and deep injuries. Using selected factors, comparisons of the classification performance of each model indicated RBF-SVM and RF models were superior to the others. Of the SVM models, the RBF kernel model was superior to the linear kernel model; it can be inferred that the performance of the high-dimensional transformed RBF model is superior when the dimension is complex because of the use of multiple variables. The findings suggest there are limitations to analyses involving imbalanced, multidimensional original data, such as data on road traffic mortality. Thus, analyses must be performed after imbalances are corrected.

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