Road Traffic Injuries In China Research Articles

Hospitalization costs of road traffic injuries in Hunan, China: A quantile regression analysis

BackgroundHealthcare expenditure of road traffic injuries in China has not been adequately investigated so far. We aim to provide comprehensive information about the hospitalization costs of inpatients who suffered road traffic injuries, and explore the components and influencing factors of costs. MethodsWe extracted the data of all inpatients (n = 60535) with road traffic injuries during the year 2019 from Chinese National Health Statistics Network Reporting System database in Hunan, China. We calculated the components of hospitalization costs and analyzed the association between hospitalization costs and patient characteristics using quantile regression models. ResultsThe median hospitalization cost was $853.48, and the median length of hospital stay was 9 days. Vulnerable road users accounted for 84.9 % of all cases. Medicine cost is the first driver of hospitalization cost, accounting for 25.94 %. In the low- and medium-cost groups, hospitalization costs were highly concentrated on diagnosis, medicine, and medical services, while in the high-cost groups, consumable cost constituted the highest percentage. Male, a longer length of stay, more severe injuries, two or more comorbidities, surgical treatment, and admission to tertiary hospitals were significantly associated with higher hospitalization costs, and the regression coefficients increased with increasing of quartile points. Costs were lower in the 0–14 years group than in the other groups across all quartiles. At the median, occupants of heavy transport vehicle incurred the highest costs, $44.18 higher than pedestrians; injuries at lower extremities generated higher costs than those at any other site; and vascular injuries caused the greatest costs, $786.24 higher than superficial injuries. ConclusionsRoad traffic injuries cause huge healthcare costs for victims, most of whom are vulnerable road users. The total cost of hospitalization is incurred mainly for medicine, consumables, diagnosis, medical services, and treatment. Patients’ demographic factors (gender and age), clinical factors (injury severity, location, nature, and number of comorbidities), treatment factors (surgery, length of stay, and hospital level), and road user type are all significantly associated with hospitalization costs.

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.

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.

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

BackgroundRoad 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.ObjectivesThe 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.MethodsRoad 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.ResultsOver 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.ConclusionEven 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.

Road traffic mortality in China: analysis of national surveillance data from 2006 to 2016

Sustainable Development Goal (SDG) 3.6 is to halve the number of global deaths and injuries from road traffic accidents by 2020. We aimed to investigate progress in reducing mortality rates from road traffic injury in China from 2006 to 2016. We obtained data from national disease surveillance points. Crude and age-standardised mortality were calculated, with SEs. Joinpoint regression analysis was used to examine and quantify trends in overall and subgroup road traffic mortality from 2006 to 2016. Subgroup analyses were done by place (urban and rural), sex, age group, geographical location (province), and road user, and by type of vehicle for motor vehicle occupant deaths. In 2016 in China, crude road traffic mortality was 11·0 (SE 0·11) deaths per 100 000 population. Overall age-adjusted road traffic mortality increased from 12·6 (SE 0·03) deaths per 100 000 population in 2006 to 15·5 (0·03) deaths per 100 000 population in 2011 then decreased to 10·4 (0·03) deaths per 100 000 population in 2016. Subgroup mortality rates generally followed similar trends. Males, older adults, and rural areas consistently had higher road traffic mortality rates than did females, younger people, and urban areas. Mortality changes varied across urban and rural areas and by sex, age group, and province between 2006 and 2016, revealing large urban-rural and provincial disparities and highlighting pedestrians as the most vulnerable road users. Deaths among occupants of cars and three-wheeled motor vehicles constituted 48% and 20%, respectively, of total occupant mortality from road traffic accidents between 2006 and 2016. Despite a substantial decrease in road traffic mortality since 2011, the SDG target to halve deaths and injuries from road traffic accidents by 2020 is unlikely to be reached in China. Systematic and sustainable efforts are needed to accelerate progress in road traffic safety in China. National Natural Science Foundation of China.

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

Objectives: This study analyzed the characteristics and burdens of road traffic injuries (RTIs) from the 3 perspectives of time, space, and population in China and predicted traffic fatalities using 2 models.Methods: By extracting data from the China Statistical Yearbooks and GBD 2015 (Global Health Data Exchange), we described the change in the time trend of traffic crashes and economic losses associated with the rate of motorization in China from 1996 to 2015; analyzed the geographical distribution of these events by geographic information system; and evaluated the age-, sex-, and cause-specific death rate, disability-adjusted life year (DALY) rate, years of life lost (YLL) rate, and years lost due to disability (YLD) rate lost from RTIs from 1990 to 2015. In addition, we predicted the traffic fatality (per population or vehicles) trend using the log-linear model derived from Smeed’s and Borsos’ models.Results: From 1996 to 2015, the motorization rate showed rapid growth, increasing from 0.023 to 0.188. With the growth in the motorization rate, the time trends of traffic crashes and economic losses in China changed, showing a tendency to first increase and then later decrease. The crashes and losses were closely correlated and mainly distributed in some of the economically developed provinces, including Zhejiang, Jiangsu, Anhui, Sichuan, and Guangdong provinces. The health burden of RTIs presented a time trend similar to that of the economic burden, and it was higher among males than females. The death rate among older pedestrians was higher. The DALY rate and YLL rate among young and middle-aged pedestrians were higher. The YLD rate among older motor vehicle drivers was higher. In addition, the fatalities per 10,000 vehicles continued to decline, and Borsos’s model was better fitted to the reported traffic fatalities than Smeed’s model.Conclusions: Although the burden of RTIs in China has declined, the burden of RTIs is still heavy. Hence, RTIs remain a universal problem of great public health concern in China, and we need to work hard to reduce them.

Prevalence of drink driving and speeding in China: a time series analysis from two cities

ObjectivesTo confront the public health challenge imposed by road traffic injuries in China. Study designA consortium of international partners designed and implemented targeted interventions, such as social media campaigns, advocacy for legislative change and law enforcement training, to reduce the percentage of drink driving and speeding in two Chinese cities, Dalian and Suzhou, from 2010 to 2014. MethodsTime series models were developed to detect changes in the prevalence of drink driving and speeding using data collected through four years of observational studies. ResultsThis analysis, based on 15 rounds of data, shows that from May 2011 to November 2014, the percentage of vehicles driving above the speed limit decreased from 31.8% (95% confidence interval [CI]: 29.2–34.5) to 7.4% (95% CI: 7.0–7.9) in Dalian and from 13.5% (95% CI: 11.7–15.5) to 6.9% (95% CI: 6.4–7.4) in Suzhou. Drink driving decreased from 1.7% (95% CI: 1.1–2.4) in January 2011 to 0.5% (95% CI: 0.2–0.9) in November 2014 in Dalian and from 6.4% (95% CI: 5.4–7.4) to 0.5% (95% CI: 0.1–2.4) in Suzhou during approximately the same period. Time series models confirmed declining trends in both risk factors in both cities (P-value: 0.06 for speeding prevalence in Suzhou; all other P-values are below 0.05). Disaggregated by vehicle type, saloon cars and SUVs were more likely to exceed the posted speed limit than other types of vehicles in both cities. The speeding rate was higher where the posted speed limit is lower. In Dalian, more drivers were driving above the posted speed limit on weekdays than on weekends (11.4% vs 6.8%); Suzhou had a similar pattern, but the difference was smaller (14.0% vs 12.2%). ConclusionDespite the challenge in accurately attributing the observed changes to one programme, the substantial reduction in the prevalence of the two risk factors suggests that through coordinated actions, internationally recognized best practices in road safety may be effective in improving road traffic safety in China.

Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model

PurposeRoad traffic injuries have become a major public health problem in China. This study aimed to develop statistical models for predicting road traffic deaths and to analyze seasonality of deaths in China. MethodsA seasonal autoregressive integrated moving average (SARIMA) model was used to fit the data from 2000 to 2011. Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were used to evaluate the constructed models. Autocorrelation function and partial autocorrelation function of residuals and Ljung–Box test were used to compare the goodness-of-fit between the different models. The SARIMA model was used to forecast monthly road traffic deaths in 2012. ResultsThe seasonal pattern of road traffic mortality data was statistically significant in China. SARIMA (1, 1, 1) (0, 1, 1)12 model was the best fitting model among various candidate models; the Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were −483.679, −475.053, and 4.937, respectively. Goodness-of-fit testing showed nonautocorrelations in the residuals of the model (Ljung–Box test, Q = 4.86, P = .993). The fitted deaths using the SARIMA (1, 1, 1) (0, 1, 1)12 model for years 2000 to 2011 closely followed the observed number of road traffic deaths for the same years. The predicted and observed deaths were also very close for 2012. ConclusionsThis study suggests that accurate forecasting of road traffic death incidence is possible using SARIMA model. The SARIMA model applied to historical road traffic deaths data could provide important evidence of burden of road traffic injuries in China.

Autoregressive integrated moving average model in predicting road traffic injury in China

This research aimed to explore the application of autoregressive integrated moving average (ARIMA) model of time series analysis in predicting road traffic injury (RTI) in China and to provide scientific evidence for the prevention and control of RTI. Database was created based on the data collected from monitoring sites in China from 1951 to 2011. The ARIMA model was made. Then it was used to predict RTI in 2012. The ARIMA model of the RTI cases was Yt = e(Y˙t-1+0.456▿Yt-1+et) (et stands for random error). The residual error with 16 lags was white noise and the Ljung-Box test statistic for the model was no statistical significance. The model fitted the data well. True value of RTI cases in 2011 was within 95% CI of predicted values obtained from present model. The model was used to predict value of RTI cases in 2012, and the predictor (95%CI) was 207 838 (107 579-401 536). The ARIMA model could fit the trend of RTI in China.

Bicyclist mortality between 2006 and 2010 in China: findings from national Disease Surveillance Points (DSP) data

ContextWhile road traffic mortality has been reported to be seriously undercounted by the police in China, non–police-reported data have not been explored previously to examine vulnerable road user mortality.ObjectiveTo examine...

Road Traffic Injury in China: A Review of National Data Sources

Objective: Road traffic injury (RTI) has become one of the leading causes of deaths in China, yet numbers on road traffic deaths are often inconsistent. This study sought to systematically review 4 national-level data sources that can be used to estimate burdens of RTI, including mortality, injury, and crashes in China. Methods: We conducted structured literature reviews in PubMed, using combined key words of injury or fatality or injury surveillance and traffic and China in order to identify relevant studies (in both English and Chinese) and data sources. We also conducted interviews and hosted seminars with key researchers from the Chinese Center for Disease Control and Prevention (Chinese CDC) to identify potential useful data sources for injury surveillance. We then extracted key information from publicly available reports of each data source. Results: Four national-level data sources were reviewed and compared: Ministry of Health-Vital Registration (MOH-VR) System, Chinese CDC-Disease Surveillance Points (DSP), Chinese CDC-National Injury Surveillance System (NISS), and police reports. Together they provide a complementary yet somewhat contradictory epidemiological profile of RTIs in China. Estimates on road traffic fatalities obtained from MOH-VR and police reports are often used by researchers and policymakers, whereas DSP and NISS, both with great merits, have virtually not been used for RTI research. Despite the well-documented problems of underreported deaths with both MOH-VR and DSP, estimated road traffic deaths from both systems were 3 times those reported by the police. Conclusions: As the foundation of injury prevention, national-level data sources and surveillance systems were reviewed in the study. Existing data infrastructures present the Chinese government a great opportunity to strengthen and integrate existing surveillance systems to better track road traffic injury and fatality and identify the population at risk.

Road Traffic Injuries in the People's Republic of China, 1951–2008

Objective: Road traffic injuries (RTIs) have become the leading cause of injury deaths in China. This article analyzed the trends in all crashes, nonfatal injuries, and fatalities from road traffic crashes from 1951 to 2008 and compared the crash frequency, crash severity, and crash patterns by provinces, types of road, and injured road users. Methods: Road traffic crash data were obtained from the Bureau of Traffic Management at the Ministry of Public Security and National Bureau of Statistics of China. Descriptive statistical analyses were conducted. Results: Over the past 5 decades, road traffic injuries have increased substantially in China. From 1951 to 2008, the total number of road traffic crashes, nonfatal injuries, and fatalities increased by 43-fold, 58-fold, and 85-fold, respectively. Linear regression suggested a significant decline of 30.1 percent (95% confidence interval [CI]: 24.8–35.3) in the mortality rate per 100,000 people during the period 2002 to 2008. From 2004 to 2008, road traffic crash mortality rate per 100,000 people varied greatly in China from the lowest of 3.0 in Henan in 2008 to the highest of 21.7 in Xizang in 2004. RTIs in China disproportionally affected the following populations: males, persons 21 to 50 years of age, pedestrians, and motorcyclists/bicyclists. Adults aged more than 65 years accounted for approximately 10 percent of total road traffic deaths. Road types and RTIs severity were closely related; highways were associated with greater mortality rates. Conclusion: Road traffic injuries have become a burgeoning public health problem in China. Programs need to be developed to prevent nonfatal injuries and fatalities caused by road traffic crashes in this emerging country.

Comparing road traffic mortality rates from police-reported data and death registration data in China

To compare death rates from road traffic injuries in China in 2002-2007 when derived from police-reported data versus death registration data. In China, police-recorded data are obtained from police records by means of a standardized, closed-ended data collection form; these data are published in the China statistical yearbook of communication and transportation. Official death registration data, on the other hand, are obtained from death certificates completed by physicians and are published in the China health statistics yearbook. We searched both sources for data on road traffic deaths in 2002-2007, used the χ(2) test to compare the mortality rates obtained, and performed linear regression to look for statistically significant trends in road traffic mortality over the period. For 2002-2007, the rate of death from road traffic injuries based on death registration data was about twice as high as the rate reported by the police. Linear regression showed a significant decrease of 27% (95% confidence interval, CI: 35-19) in the death rate over the period according to police sources but no significant change according to death registration data. The widely-cited recent drop in road traffic mortality in China, based on police-reported data, may not reflect a genuine decrease. The quality of the data obtained from police reports, which drives decision-making by the Government of China and international organizations, needs to be investigated, monitored and improved.

An epidemiological study on road traffic injury in China

ObjectiveTo explore the influencing factors and secular of road traffic injury (RTI) in China, and to furnish evidence to formulate strategy and measures to improve RTI.MethodsBy using some indices such...

The association of road safety knowledge, risk behaviour and road traffic injury among school-aged children in Guangzhou, China

ObjectiveResearch on road traffic injuries in China is growing, but little has focused on children. This study describes road traffic injuries among school-aged children in Guangzhou, China and examines the...

Road traffic injuries in the Peoples Republic of China, 1951–2008

BackgroundRoad traffic injuries (RTIs) have become the leading cause of injury deaths in the P.R. China. We analysed the trends and characteristics of RTIs from 1951 to 2008.MethodsRoad traffic crash...