Injury Characteristics and Request for Discharge against Medical Advice among Road Trauma Patients | Chapter 10 |Medical Science: Updates and Prospects Vol. 3

 

Background: Discharge Against Medical Advice (DAMA) occurs when a patient leaves the hospital contrary to the managing physician's recommendation. DAMA, with its attendant effects of increased healthcare cost and mortality, is common globally. Therefore, DAMA must be continuously brought into public discourse, and strategies to address it should be explored.

 

Aim: The aim of this study was to highlight the Patterns of injuries by Road Traffic Accident victims, the implications of discharge against Medical Advice on certain critical injuries, and the importance of follow-up.

 

Methods: A retrospective study was carried out at the Federal Medical Centre Makurdi, a tertiary health facility in North Central Nigeria, from January 2021 to March 2022. Medical records (case notes) of 121 patients who were involved in Road Traffic Accidents (RTAs), and were admitted either into the ward through the Accident and Emergency Unit, or at the Accident and Emergency Unit of the hospital, were retrieved and analyzed against such variables as age, sex, occupation, resident, types of injury, and length of hospital stay.  Descriptive Statistics were deployed using Microsoft Excel and Statistical Package for Social Sciences (SPSS) to analyse the data.

 

Results: DAMA rate was 12.4% (n=15), of which 8 (6.6%) were females, and 7 (5.7%) were males. Head injury was the commonest injury, 8 (53.3%), seen, while musculoskeletal injuries were 7 (46.7%). The majority of the victims, 6, were in the age group, 30-39 years, with an average age of 37.2. Farmers and housewives were the dominant occupational groups involved. The average hospital stay was 6 days. There was no form of health insurance coverage or follow-up protocol.

 

Conclusion: DAMA for critically injured patients is common in our environment, and there is no known established follow-up system. There is no health insurance coverage. Physicians must strive to track and follow up on these patients to improve outcomes.

 

 

Author(s) Details

Suega P. Inunduh
Department of Human Anatomy/Surgery, Veritas University Abuja, Nigeria.

 

Anthony A. Amuta
Department of Surgery, Federal Centre Makurdi, Nigeria.

 

 

Please see the book here :- https://doi.org/10.9734/bpi/msup/v3/6808

Epidemiological Study of Road Traffic Accident Cases Admitted in a Tertiary Care Hospital in Andhra Pradesh, India| Chapter 3 | Current Innovations in Medicine and Medical Science Vol. 8

 The present explanatory study was conducted to see the various epidemiological determinants related to road traffic casualty cases admitted in a after second care hospital in Andhra Pradesh from 1st July 2008 to 30 th June 2009 Road accidents are human disaster. They involve extreme human suffering and finances costs in terms of eventually deaths, injuries and deficit of potential income. They are not any more accidental. Deaths from drive traffic injuries (RTI), specifically motor vehicle traffic accidents (MVTA), have existed described as a general hidden epidemic affecting all portions of society. Total 436 RTA sufferers were included in the study. There were 87.61% male and 12.39% female calamity victims. Labourers were the capital (36.01%) among the sufferers. The highest number of accidents took place in the period of January (14.45%) and on Sundays (19.26%). 31.36% pedestrians were harmed by motorized two wheelers, 20.34% by truck. Among the trainers of different types of taxis, Motorized two wheeler drivers were casualties in 31.1% cases Out of 134drivers 17.16.9% were found to have absorbed alcohol. Being knocked down was the low mode of accidents. The authentic pressure and motivation to develop driving abilities can come only through licensing experts by adopting stricter, more inclusive and scientifically based test laying a stress on course rules, regulations and traffic signal devices.

Author(s) Details:

Pravin N. Yerpude,
Department of Community Medicine, Chhindwara Institute of Medical Sciences, Chhindwara (M.P.), India.

Keerti S. Jogdand,
Department of Community Medicine, Chhindwara Institute of Medical Sciences, Chhindwara (M.P.), India.
 
Please see the link here: https://stm.bookpi.org/CIMMS-V8/article/view/8720

Weighted Fuzzy Time Series Model to Forecast Epidemic Injuries and its Data Visualization | Chapter 5 | Current Overview on Science and Technology Research Vol. 5

 Globally, the coronavirus disease spread rapidly. To aid in decision-making, we must develop precise future projections of the number of COVID-19 infections. On the basis of accumulating global experience from which we are working to enhance our future methodologies to deal with such a pandemic and in an effort to find the best application, we present some of the techniques that have shown useful in predicting the number of people infected with COVID-19 in a specific period in different regions of the world in this paper.

We use a rewarding model to predict injuries in areas with COVID-19, particularly in the Arab region.

This prediction is based on Saudi Arabia's epidemic injury statistics from March 2 to July 20, 2020. Charts and graph visualisation are two alternative techniques to present time series data. We propose the use of weighted fuzzy time series methods (WFTS) and weighted non-stationary fuzzy time series techniques to compare with the conventional Auto-Regressive Integrated Moving Average (ARIMA) statistical method (WNSFTS). Before utilising the (ARIMA) and (WFTS) algorithms to forecast the given data, stationary data conversion is required because the data is not stationary. On our injuries dataset, we log transform and differentiate. The graphic depicts the situation for COVID in Saudi Arabia as improving. When we analyse the original data using the Dickey-Fuller Test (DFT), we find that the p-value is equal to 0.646, which is higher than 0.05 and shows non-stationarity. The mean square error (MSE), root mean square error (RMSE), and normalisation root mean square error (NRMSE) are used to compare the accuracy of the different techniques. The results show that WFTS approaches offer reliable assistance for calculating COVID-19-based pandemic injuries in the area. The use of Weighted Non Stationary Fuzzy Time Series (WNSFTS) can produce significantly better results when projecting the issue of epidemic injuries. because it can anticipate future infections and has a high level of predictive accuracy.

Author(s) Details:

Hala Ahmed Abdul-Moneim,
Department of Mathematics, Darb University College, Jazan University, Jazan, Saudi Arabia and Department of Mathematics, Faculty of Science, Minia University, Minia, Egypt.

Please see the link here: https://stm.bookpi.org/COSTR-V5/article/view/8335