An analysis of the characteristics of road traffic injuries and a prediction of fatalities in China from 1996 to 2015

Global burden and trends of transport injuries from 1990 to 2019: an observational trend study

BACKGROUND

Transport injuries (TIs) are a major cause of global disability-adjusted life-years (DALYs) and mortality. In this study, we aimed to assess the global burden and trends of TIs from 1990 to 2019.

METHODS

We assessed the annual age-standardised incidence rate (ASIR) and age-standardised DALYs rate of TIs by sex, age, Social Development Index (SDI) and geographical region from 1990 to 2019 from the Global Burden of Disease Study 2019. The changing trends were described by estimated annual percentage changes (EAPCs).

RESULTS

Globally, in 2019, the ASIR and age-standardised DALYs rates of TIs were 134 6.06/100 000 (95% UI 11 42.6/100 000-157 5.57/100 000) and 97 7.91/100 000 (86 8.91/100 000-107 6.81/100 000), respectively. From 1990 to 2019, the global ASIR of TIs presented significant upwards trends with the EAPC (0.25%, 95% CI 0.19% to 0.31%), and it was significantly increased in the age groups of 15-49 (0.37%, 95% CI 0.29% to 0.45%), 50-69 (0.40%, 95% CI 0.36% to 0.44%) and 70+ (0.22%, 95% CI 0.17% to 0.28%). Prominent increases in ASIR were detected in middle-SDI areas (0.72%, 95% CI 0.57% to 0.87%), low-middle SDI areas (0.66%, 95% CI 0.59% to 0.72%) and low-SDI areas (0.21%, 95% CI 0.17% to 0.26%). The global age-standardised DALYs rate presented downwards trends with the EAPC (-1.27%, 95% CI -1.35% to -1.2%), and it was significantly decreased in all age groups and SDI areas.

CONCLUSION

Globally, TIs still cause a serious burden, and the incidence has significantly increased, especially in people above the age of 14 and in middle-SDI and low-SDI areas, thus necessitating more attention and health interventions.

A dataset of Chinese drivers' driving behaviors and socio-cultural factors related to driving

Given the high fatality rate due to road traffic accidents in China, understanding the factors influencing aggressive driving behaviors among Chinese drivers is essential to alleviate the problem. The paper describes a dataset of 1039 Chinese drivers' driving behaviors and the socio-cultural factors associated with the behaviors. The dataset was collected through an online survey. The dataset comprises five main categories: 1) driving information, 2) aggressive driving behaviors, 3) friend/peer influence, 4) family influence, and 5) socio-demographic information. The dataset is valuable for public health and transportation researchers to explore factors influencing drivers' driving behaviors and public safety in China. The dataset's construct validity was confirmed by the Bayesian Mindsponge Framework (BMF) analytics. Specifically, the analysis shows that safe driving behaviors are affected by information promoting safe driving that is passively and actively absorbed from friends/peers (friends/peers being role models and friends'/peers' support, respectively). The result is consistent with the Mindsponge Theory's information-processing mechanism in human minds.

Temporal and spatial trends in road traffic fatalities from 2001 to 2019 in Shandong Province, China

OBJECTIVE

This study explored the temporal and spatial trends in road traffic fatalities in Shandong Province from 2001 to 2019 and discusses the possible influencing factors.

METHODS

We collected data from the statistical yearbooks of the China National Bureau of Statistics and the Shandong Provincial Bureau of Statistics. Join-point Regression Program 4.9.0.0 and ArcGIS 10.8 software were used to analyze the temporal and spatial trends.

RESULTS

The mortality rate of road traffic injuries in Shandong Province decreased from 2001 to 2019, with an average annual decrease of 5.8% (Z = -20.7, P < 0.1). The three key time points analyzed in the Join-point regression model roughly corresponded to the implementation times of traffic laws and regulations in China. The temporal trend in case fatality rate in Shandong Province from 2001 to 2019 was not statistically significant (Z = 2.8, P < 0.1). The mortality rate showed spatial autocorrelation (global Moran's I = 0.3889, Z = 2.2043, P = 0.028) and spatial clustering. No spatial autocorrelation was observed in the case fatality rate (global Moran's I = -0.0183, Z = 0.2308, P = 0.817).

CONCLUSIONS

The mortality rate in Shandong Province decreased significantly over the studied period, but the case fatality rate did not decline significantly and remains relatively high. Many factors influence road traffic fatalities, among which laws and regulations are the most important.

Characteristics, likelihood and challenges of road traffic injuries in China before COVID-19 and in the postpandemic era

Through a review of previous studies, this paper analysed the epidemiological characteristics and attempts to determine the various trends of road traffic injuries (RTIs) in China before and after the coronavirus disease 2019 (COVID-19). This paper proposed effective measures and suggestions for responding to RTIs in China. Moreover, this paper aimed to provide some references for studies on RTIs in the future. According to a reference review, 50 articles related to RTIs were published and viewed in the China National Knowledge Infrastructure (CNKI), Wanfang database, Weipu (VIP) database and PubMed/MEDLINE database. Articles were selected according to the exclusion and inclusion criteria and then classified and summarized. Regarding cases, RTIs in China were highest in summer, autumn, and in rural areas and lowest in February. Men, elderly individuals and people living in rural areas were more susceptible to RTIs. In addition, thanks to effective and proactive policies and measures, the number of RTIs and casualties in China has substantially decreased, while there has been a growing number of traffic accidents along with the increase in nonmotor vehicles. However, it is worth noting that the number of RTIs obviously fell during the COVID-19 pandemic due to traffic lockdown orders and home quarantine policies. Nevertheless, accidents related to electric bicycles increased unsteadily because of the reduction in public transportation use at the same time. The factors that cause RTIs in China can be divided into four aspects: human behaviours, road conditions, vehicles and the environment. As a result, measures responding to RTIs should be accordingly proposed. Moreover, the road traffic safety situation in developing countries was more severe than that in developed countries. RTIs in China showed a downward trend attributed to road safety laws and various policies, and the downward trend was more significant during the COVID-19 pandemic owing to traffic lockdowns and home quarantine measures. It is urgent and necessary to promote road traffic safety, reduce injuries, and minimize the burden of injuries in developing countries.

The comparative analysis of SARIMA, Facebook Prophet, and LSTM for road traffic injury prediction in Northeast China

Objective

This cross-sectional research aims to develop reliable predictive short-term prediction models to predict the number of RTIs in Northeast China through comparative studies.

Methodology

Seasonal auto-regressive integrated moving average (SARIMA), Long Short-Term Memory (LSTM), and Facebook Prophet (Prophet) models were used for time series prediction of the number of RTIs inpatients. The three models were trained using data from 2015 to 2019, and their prediction accuracy was compared using data from 2020 as a test set. The parameters of the SARIMA model were determined using the autocorrelation function (ACF) and the partial autocorrelation function (PACF). The LSTM uses linear as the activation function, the mean square error (MSE) as the loss function and the Adam optimizer to construct the model, while the Prophet model is built on the Python platform. The root mean squared error (RMSE), mean absolute error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the predictive performance of the model.

Findings

In this research, the LSTM model had the highest prediction accuracy, followed by the Prophet model, and the SARIMA model had the lowest prediction accuracy. The trend in medical expenditure of RTIs inpatients overlapped highly with the number of RTIs inpatients.

Conclusion

By adjusting the activation function and optimizer, the LSTM predicts the number of RTIs inpatients more accurately and robustly than other models. Compared with other models, LSTM models still show excellent prediction performance in the face of data with seasonal and drastic changes. The LSTM can provide a better basis for planning and management in healthcare administration.

Implication

The results of this research show that it is feasible to accurately forecast the demand for healthcare resources with seasonal distribution using a suitable forecasting model. The prediction of specific medical service volumes will be an important basis for medical management to allocate medical and health resources.

Characteristic and Introspection of Road Traffic Injuries in China from 2012 to 2017

Background

To analyze whether the area differences of RTIs (road traffic injuries, RTIs) caused by unequal development in China, provide suggestions for the prevention of the RTIs.

Methods

The data of RTIs in China was collected from the authoritative official website and yearbook of China.

Results

Total RTIs in the East was the highest (RTIs frequency: 591789; injured people: 600611; death toll: 168885; economic loss: 27.22 billion RMB), followed by the Center (RTIs frequency: 321807; injured people: 352769; death toll: 91966; economic loss: 23.90 billion RMB) and the lowest in the West (RTIs frequency: 289482; injured people: 332517; death toll: 101095; economic loss: 16.35 billion RMB). The multivariate linear correlation and regression showed that the characteristic of RTIs was highly related with GDP (r=0.99, P < 0.001).

Conclusion

The economically developed areas had a large amount of traffic damages. The government should focus on preventing high RTIs in the East and high death tolls in the West.

Road injury burden in Mexico 1990 to 2019: Secondary data analysis from the Global Burden of Disease Study

Road injuries have been a major cause of premature mortality and disability in Mexico. The objective of this paper is to report the findings from the Global Burden of Disease study (GBD-2019) on road injuries in Mexico at a national and subnational scale from 1990 to 2019, and to assess the association between road injury burden and the socio-demographic index. Following the 2019 Global Burden of Disease study road injury mortality, premature mortality, the years lived with disability and disability-adjusted life-years (DALYs) are reported. While the number of deaths from road injuries increased between 1990 and 2019, the age-standardized mortality rates declined. Pedestrian road injuries and motor vehicle road injuries accounted for 8 of every 10 deaths from road injury in 2019. Road injury mortality and DALY rates decreased nationally, but stagnated since 2011. The road injury burden was higher for men in all age groups. Pedestrian and motor vehicle road injuries caused the highest DALY rate in both males and females. There was no significant association between the SDI and the road injury age-standardized DALY rates. This study presents a comprehensive report of road injury burden of disease in Mexico. Mexico continues to have an incomplete, fragmented and poorly enforced legislative framework, with a large diversity between its 32 states. Thus, an integrated legislative and juridical effort is needed to continue reducing the road injury disease burden, which is tailored for specific age groups, vulnerable road users and high-burden areas.

miRNA-92a-3p regulates osteoblast differentiation in patients with concomitant limb fractures and TBI via IBSP/PI3K-AKT inhibition

Patients who sustain concomitant fractures and traumatic brain injury (TBI) are known to have significantly quicker fracture-healing rates than patients with isolated fractures. The mechanisms underlying this phenomenon have yet to be identified. In the present study, we found that the upregulation of microRNA-92a-3p (miRNA-92a-3p) induced by TBI correlated with a decrease in integrin binding sialoprotein (IBSP) expression in callus formation. In vitro, overexpressing miRNA-92a-3p inhibited IBSP expression and accelerated osteoblast differentiation, whereas silencing of miRNA-92a-3p inhibited osteoblast activity. A decrease in IBSP facilitated osteoblast differentiation via the Phosphatidylinositol 3-kinase/threonine kinase 1 (PI3K/AKT) signaling pathway. Through luciferase assays, we found evidence that IBSP is a miRNA-92a-3p target gene that negatively regulates osteoblast differentiation. Moreover, the present study confirmed that pre-injection of agomiR-92a-3p leads to increased bone formation. Collectively, these results indicate that miRNA-92a-3p overexpression may be a key factor underlying the improved fracture healing observed in TBI patients. Upregulation of miRNA-92a-3p may therefore be a promising therapeutic strategy for promoting fracture healing and preventing nonunion.

Forecasting deaths of road traffic injuries in China using an artificial neural network

Objectives: This study was conducted to estimate road traffic deaths and to forecast short-term road traffic deaths in China using the Elman recurrent neural network (ERNN) model.Methods: An ERNN model was developed using reported police data of road traffic deaths in China from 2000 to 2017. Different numbers of neurons of the hidden layer were tested and different combinations of subgroup datasets have been used to develop the optimal ERNN model after normalization. The mean absolute error (MAE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE) were measures of the deviation between predicted and observed values. Predicted road traffic deaths from the ERNN model and the seasonal autoregressive integrated moving average (SARIMA) model were compared using the MAPE.Results: By comparing the MAE, RMSE and MAPE of different numbers of hidden neurons and different ERNN models, the ERNN model provided the best result when the input neurons were set to 3 and hidden neurons were set to 10. The best validated neural model (3:10:1) was further applied to make predictions for the latest 12 months of deaths (MAPE = 4.83). The best SARIMA (0, 1, 1) (0, 1, 1)₁₂ model was selected from various candidate models (MAPE = 5.04). The fitted road traffic deaths using the two selected models matched closely with the observed deaths from 2000 to 2016. The ERNN models performed better than the SARIMA model in terms of prediction of 2017 deaths.Conclusions: Our results suggest that the ERNN model could be utilized to model and forecast the short-term trends accurately and to evaluate the impact of traffic safety programs when applied to historical road traffic deaths data. Forecasting traffic crash deaths will provide useful information to measure burden of road traffic injuries in China.

Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?

Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics.

Seating preferences in highly automated vehicles and occupant safety awareness: A national survey of Chinese perceptions

Objective: The potential challenge for providing occupant protection accompanying seating preferences is an essential safety prerequisite for highly automated vehicle (HAV) popularization. This research is aimed toward identifying Asia-specific individualized seating preferences in HAVs and occupant safety awareness via a national survey in China.Methods: An online questionnaire survey was performed to investigate seating preferences (i.e., sitting posture, seating orientation, and position) and occupant safety awareness (i.e., seat belt usage and receptiveness to extended or additional restraints beyond the conventional three-point seat belt). We assessed whether perceptions were modulated by individual characteristics via bivariate and correlation analyses. The possibility of wearing seat belts was estimated by binary logistic regression.Results: The final survey data set includes 1,018 respondents after a rigorous validity check (response rate: 59.2%). The results show that preferred sitting postures and seating orientation were significantly associated with sociodemographic characteristics (e.g., gender, age, city tier) (p < 0.05). The rear seat was preferred in both the conventional (65.6%) and "face-to-face mode" seating configurations (77.6%), largely due to the fact that customers subjectively viewed it as being safer than sitting in a front seat in case of collisions. Despite the current trend of an increasing usage rate of seat belts, 48.5% of respondents preferred to be unrestrained in rear seats, especially for the subgroups who were from less developed cities and with a higher usage rate of public transport (p < 0.01). Low receptiveness to extended restraint and high comfort requirements were confirmed for the young, high-frequency road users, and for those who were from developed areas (p < 0.05).Conclusions: Diversified and specific seating preferences of Chinese occupants were identified facing emerging use of HAVs. Next generation occupant protection systems shall be adapted to account for the individualized expectations and needs on seating designs from certain population groups. Balanced restraint design between safety and comfort was required to exceed the existing strong dependence on exogenous causes of restraint use (e.g., legal restrictions) in Asia.

Road traffic injuries in China from 2007 to 2016: the epidemiological characteristics, trends and influencing factors

Background

Road traffic accidents are one of the serious disasters that cause public injury, fatality and great economic loss. They are a growing public health problem around the world.

Objectives

The aim of this study was to determine epidemiological characteristics, tendency and possible influencing factors of road traffic injuries (RTIs) in China, so as to give target suggestions on preventative measures.

Methods

Road traffic accident data were obtained from National Bureau of Statistics of China and Ministry of Transport of the People’s Republic of China. Descriptive statistic such as RTIs frequency, trends of different accident types from 2007 to 2016; the RTIs difference between different regions and road surfaces were compared; and the possible influencing factors of RTIs were also explored.

Results

Over the past decade, with the mileage of constructed highway increased, the frequency of road traffic accidents have declined substantially in China, and the death toll from road traffic accidents with motor vehicles has declined from 2007 to 2015, Conversely, the number of deaths from non-motor vehicle accidents has risen rapidly since 2012. Our study showed that the traffic accident related mortality in Guizhou province was different from the level of the whole nation, and the Eastern, Central and Western areas of China were all significantly different (P < 0.001). Linear regression suggested a significant affected of gross domestic product (GDP)-per-capita, education level, the number of health institutions, populations, and car ownership status on traffic accident death tolls (P < 0.001). Moreover, cement concrete pavement roads were associated with the highest occurrence rates of RTI, and RTIs was statistically significant (P < 0.001) on different road surfaces.

Conclusion

Even though the frequency of road traffic accidents has declined, RTIs remain an urgent public health problem in China. Thus, the government should give some target preventative measures to reduce RTIs, aiming at different regions, the increasing trend of the death toll related to non-motor vehicles and the highest occurrence on cement concrete pavement roads.

Comparison of Secular Trends in Road Injury Mortality in China and the United States: An Age-Period-Cohort Analysis

This study aimed to identify and compare the mortality trends for road injuries in China and the United States, and evaluate the contributions of age, period, and cohort effects to the trends from 1990 to 2014. Using the 2016 Global Burden of Disease Study database, the mortality trends were analyzed by joinpoint regression and age-period-cohort modeling. Overall, the mortality for road injuries was higher in China than in the United States. The mortality in China increased from 1992 to 2002 (annual percent change [APC] was 1.9%), and then decreased from 2002 to 2015 (APC_2002–2009 was 1.5%; APC_2009–2015 was 3.5%). For the United States, the mortality decreased from 1990 to 2010 (APC_1990–1997 was 1.8%; APC_1997–2005 was 0.7%; APC_2005–2010 was 4.2%). Age-period-cohort modeling revealed significant period and cohort effects. Compared with the period 2002–2004, the period risk ratios (RRs) in 2010–2014 period declined by 14.62% for China and 18.86% for the United States. Compared with the 1955–1959 birth cohort, the cohort RRs for China and the United States in the 2010–2014 cohort reduced by 47.60% and 75.94%, respectively. Period and cohort effects could not be ignored for reducing road injury mortalities.