Differential patterns, trends and hotspots of road traffic injuries on different road networks in Vellore district, southern India

Magnitude and outcome of road traffic accidents among patients admitted in dessie town governmental hospitals, Northeast Amhara, Ethiopia, 2022

BACKGROUND

Road traffic accidents(RTA) are a major public health problem worldwide, accounting for almost 1.24 million deaths per year and it is the number one cause of death among those aged group 15-29 years. Even though there are great benefits from access to road transportation there also poses a great challenge in the individual's daily activities ranging from minor injury to death.

OBJECTIVE

This study aimed to assess the magnitude and outcome of road traffic accidents among patients admitted in Dessie Town Governmental Hospitals, Northeast Amhara, Ethiopia, 2022.

METHODS

A five-year hospital-based retrospective descriptive cross-sectional study design was conducted among 377 road traffic accident patients admitted to Dessie Town Governmental hospitals. Data were collected by simple random methods based on patient chart reviews from June 7/, 2022 to May 23/ 2017 using a checklist adapted from the WHO standard hospital-based road traffic accident questionnaires after obtaining consent from the concerned authority. EPI-Data software version 7.2 for data entry and SPSS version 25 for statistical analysis were used. Descriptive and inferential statistics were used. Statistical significance was declared at a p-value of < 0.05 with an adjusted odds ratio (AOR) and a 95% confidence interval (CI) in the final multinomial logistic regression model.

RESULTS

The magnitude of road traffic accidents was 59%, using of logistic multi nominal logistic regression we found results such that, road traffic victims who had unstable vital signs at admission (AOR = 6.4,95% CI; 2.5-16.6), didn't get prehospital treatment (AOR = 9.3,95% CI; 4-20), and severe injury (AOR = 9, 95% CI;7-15.4), had a Glasgow coma scale of 3-5 (AOR = 5.2,95% CI; 1.4-20) were found predictors for death were as unstable vital signs at admission (AOR = 3.79,95%CI;2.1-6.8), Doesn't get prehospital treatment (AOR = 2.8, 95% CI; 1.4-5.7), Hospital stay for one to two months duration (AOR = 6,95% CI;2.3-15), and greater than two months duration (AOR = 6.5,95%CI;2.5-17) were found predictors for disability among road traffic victims.

CONCLUSIONS AND RECOMMENDATIONS

Road traffic accidents constitute a major public health problem in our setting and contribute significantly to excessively high morbidity and mortality. Unstable vital signs at admission, Client doesn't get prehospital treatment, severely injured client, and had a Glasgow coma scale of 3-5 were found predictors for death were as an unstable vital sign at admission, Client doesn't get pre-hospital treatment, Hospital stays for one to two months duration, and greater than two months duration were found predictors for disability among road traffic victims.

Pattern of Road Traffic Injuries and Their Pre-hospitalization Factors Reported at a Public Tertiary Healthcare Facility and Rural Private Healthcare Facility in Rajasthan, India

OBJECTIVE

We aimed to report the pattern of road traffic injuries (RTIs) and pre-hospitalization factors of road traffic injuries among the accident victims reported at an urban and a rural healthcare facility in the Jaipur district, Rajasthan.

METHODS

This cross-sectional study was conducted in a tertiary-level, urban public healthcare facility in Jaipur city and a secondary-level, rural private facility in nearby Chomu town. The study participants were all those who encountered road traffic injury and visited any of these healthcare facilities to seek care. The study tool included information on demographics, type of road user, vehicles, accidents, roads, environment, and other pre-hospitalization factors. Data collectors were nurses trained to collect data using the tablet-based application. Data were analyzed using proportions/percentages. Bivariate analysis was done to assess the significance of differences between categories of factors and between rural and urban facilities.

RESULTS

Among 4,642 cases, 93.8% were enrolled in the urban facility, and the remaining were enrolled in the rural facility. Predominantly, males (83.9%) and young adults 18-34 years (58.9%) were reported in both study facilities. Among the accident victims reported at the urban facility, major groups were educated up to the primary level (25.1%) or graduate level (21.9%). About 60% of them were drivers. Most of these injuries occurred on urban roads (50.2%) or two-lane roads (42%). About three-fourths of the injured were using two-wheeler geared vehicles, and 46.7% were overtaking or turning the vehicle when the accident happened. The majority of cases (61.6%) did not require hospitalization. Among the rural facility participants, 27.2% were graduates, and 24.7% were below primary education. Most of these injuries happened on the national highway (35.8%) or rural roads (33.3%). Most of them used two-wheeler geared (80.1%) at the time of the accident. Most were injured while doing normal straight driving (80.5%). Most participants (80.1%) in the rural facility did not follow the traffic rules, and 43.9% required hospitalization.

CONCLUSION

Young males were the most affected age group by road traffic injuries. Differential patterns of road traffic injuries and pre-hospital factors were observed in urban and rural areas.

An Exploration of Characteristics and Time Series Forecast of Fatal Road Crashes in Manipal, India

Road crashes are the sixth leading cause of death in India. There has been a fourfold increase in the number of road traffic crashes in India in the last four decades and an increase of 9.8 times in the fatalities associated with that exponential increase. Manipal is a coastal place with a population of approximately 50,000 inhabitants lying in between the western Mountain range and the Arabian sea. The study’s objective is to explore the characteristics of fatal road crashes in Manipal from 2008–2018 using the data pertaining to fatal crashes retrieved from the office of the superintendent of police. Furthermore, it aims to forecast crashes by time series analysis prediction. The results show that most of the fatal crashes are due to exceeding the lawful speed limit, followed by driving under the influence of alcohol and going ahead and overtaking. The time series analysis forecasted the number of fatal crashes until the year 2025 and predicted that there will be an increase in the number of fatal road crashes by 4.5%. The results also provide essential leads for initiating specific intervention programmes targeting the causes of fatal road crashes.

Identify the key characteristics of pedestrian collisions through in-depth interviews: a pilot study

This study aimed to assess the feasibility of recruiting injured pedestrians from the emergency department of a major trauma centre, using an in-depth interview shortly post collision. Convenience sampling was used to prospectively recruit injured pedestrians from the Alfred Hospital Emergency and Trauma Centre. Of the 102 injured pedestrians, 39 met eligibility criteria and of these, 30 (77%) consented and completed the questionnaire. Over half of the collisions occurred at an intersection (57%), and of these the most common pre-impact vehicle manoeuvre was a vehicle turning into the street the pedestrian was crossing. In-depth interview during the early post-crash period was a feasible and effective method of collecting detailed data in an accessible sample. However, only 38% of patients met eligibility criteria. To enhance representativeness, supplementing interview data with police-reported crash data, recruiting from hospital wards and crash location assessment is recommended.

Occupational hazards and risks among commercial motorcyclists in the peri-urban city of Lagos, Nigeria

Although a significant proportion of road hazards and their associated health risks in Nigeria involve motorcycles, relatively little research explores whether commercial motorcyclists have unique seasonal accident experiences. This article analyses survey data from 241 commercial motorcyclists in selected terminals in Ifo, Nigeria, to explore how road hazards and health risks experienced differ from one season to another. The study established that seasonality of motorcycle hazards and health risks cannot be assumed across the terminals and routes as some were dominant in either rainy or dry seasons or both. In particular, most motorcycle hazards and health risks were less likely caused by seasonal variation but by human errors and road conditions. The results underscore the importance of collecting commercial motorcyclists' perceptual data because many experience hazards and health risks that are burdensome to them.

Comparison of epidemiology and injury profile between vulnerable road users and motor vehicle occupants in road traffic fatalities

Objective: Road traffic injuries (RTIs) are a major global health issue causing a global burden of mortality and morbidity. Half of all fatalities on the world's roads are vulnerable road users (VRUs). The targeted intervention strategies based on fatality analysis focusing on VRUs can effectively contribute to reducing RTIs. This study aimed to compare VRUs and motor vehicle occupants (MVOs) in terms of epidemiology and injury profile. Methods: We utilized a nationwide, prospective database of RTI-related mortality cases for patients who visited 23 emergency departments between January 2011 and December 2015. All fatalities due to RTIs in the prehospital phase or in-hospital were eligible, excluding patients with unknown mode of transport and those admitted to general wards. The primary and secondary outcomes were fracture injuries and visceral injuries diagnosed using the International Classification of Diseases, Tenth Revision (ICD-10). We compared fracture injuries between VRUs and MVOs using Abbreviated Injury Scale (AIS) 2- and 2+ classification. Results: Among a total 3,694 road traffic fatalities (RTFs), 43.3% were pedestrians, followed by MVOs (27.0%), motorcyclists (18.9), bicyclists (6.6%), and agricultural vehicle users (4.2%). The elderly (>60 years old) accounted for 54.9% of VRU fatalities. RTFs occurred most frequently in the autumn and the VRU group and the MVO group showed significant differences in weekly and diurnal variation in RTFs. The injury severities (AIS 2+) of the head, neck, and thorax were significantly different between the 2 groups (P < 0.05). Head (32.1%) and intracranial (58.6%) injuries were the most common fracture and visceral injury sites for RTFs, followed by the thorax and intrathoracic organs (25.3 and 28.8%, respectively). Conclusions: Elderly pedestrians should be targeted for decreases in RTFs, and road traffic safety interventions for VRUs should be made based on the analysis of temporal epidemiology and injury profiles of RTFs.

Assessment and prediction of road accident injuries trend using time-series models in Kurdistan

Background

Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran.

Methods

A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package.

Results

A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants' accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0)₁₂, and SARIMA (1, 1, 1) (0, 0, 1)₁₂, respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January).

Conclusion

Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the accidents during the high-risk periods in order to control and decrease the rate of the injuries.

An overview of geospatial methods used in unintentional injury epidemiology

Background

Injuries are a leading cause of death and disability around the world. Injury incidence is often associated with socio-economic and physical environmental factors. The application of geospatial methods has been recognised as important to gain greater understanding of the complex nature of injury and the associated diverse range of geographically-diverse risk factors. Therefore, the aim of this paper is to provide an overview of geospatial methods applied in unintentional injury epidemiological studies.

Methods

Nine electronic databases were searched for papers published in 2000–2015, inclusive. Included were papers reporting unintentional injuries using geospatial methods for one or more categories of spatial epidemiological methods (mapping; clustering/cluster detection; and ecological analysis). Results describe the included injury cause categories, types of data and details relating to the applied geospatial methods.

Results

From over 6,000 articles, 67 studies met all inclusion criteria. The major categories of injury data reported with geospatial methods were road traffic (n = 36), falls (n = 11), burns (n = 9), drowning (n = 4), and others (n = 7). Grouped by categories, mapping was the most frequently used method, with 62 (93%) studies applying this approach independently or in conjunction with other geospatial methods. Clustering/cluster detection methods were less common, applied in 27 (40%) studies. Three studies (4%) applied spatial regression methods (one study using a conditional autoregressive model and two studies using geographically weighted regression) to examine the relationship between injury incidence (drowning, road deaths) with aggregated data in relation to explanatory factors (socio-economic and environmental).

Conclusion

The number of studies using geospatial methods to investigate unintentional injuries has increased over recent years. While the majority of studies have focused on road traffic injuries, other injury cause categories, particularly falls and burns, have also demonstrated the application of these methods. Geospatial investigations of injury have largely been limited to mapping of data to visualise spatial structures. Use of more sophisticated approaches will help to understand a broader range of spatial risk factors, which remain under-explored when using traditional epidemiological approaches.

Electronic supplementary material

The online version of this article (doi:10.1186/s40621-016-0097-0) contains supplementary material, which is available to authorized users.