Effects of stricter drunk-driving laws on alcohol-related road traffic death, injury, and crash rates in South Korea: A synthetic counterfactual approach using Bayesian structural time-series models

Evaluating the effects of the 2017 National Health Insurance coverage expansion on amenable mortality and its disparities between areas in South Korea using Bayesian structural time-series models

To improve the low coverage rate of the National Health Insurance (NHI), South Korea implemented the NHI coverage expansion plan in 2017 to cover medically essential non-covered services and reduce copayment rates. This study aimed to estimate the effects of the 2017 NHI coverage expansion on amenable mortality and its disparities between areas in South Korea under a controlled interrupted time-series design using Bayesian structural time-series models. Age-standardized amenable mortality rates and rate differences (RDs) and rate ratios (RRs) between areas for amenable mortality were calculated monthly between July 2012 and December 2021 and used as the response series. The non-equivalent control series were monthly non-avoidable mortality rates and their regional disparities. After the coverage expansion, amenable mortality rates decreased for both males (-8.8%, 95% credible interval [CrI] -13.4% to -3.9%) and females (-8.3%, 95% CrI -13.4% to -2.4%), with the largest decline in the non-Seoul-Capital metropolitan area (-11.6%, 95% CrI -16.5% to -6.3%) rather than the Seoul Capital Area (-7.5%, 95% CrI -11.9% to -2.5%) and a non-significant reduction in the non-Seoul-Capital non-metropolitan area in females. RDs and RRs between areas for amenable mortality decreased non-significantly (-16.2%, 95% CrI -31.3% to 2.6% for RD and -1.2%, 95% CrI -3.7% to 1.5% for RR), except for a significant decrease in RD in males (-21.8%, 95% CrI -38.0% to -1.5%), and decreased less in females than in males. The coverage expansion was generally effective in reducing amenable mortality rates by area, but had limited effects in closing amenable mortality disparities between areas, favoring males and the non-Seoul-Capital metropolitan area. These results implied that additional measures are necessary to improve access to quality health care for females and underserved areas to enhance the effectiveness of the coverage expansion.

Global burden and trends of three common road injuries from 1990 to 2019 and the implications for prevention and intervention

BACKGROUND

Analysis on the burden of specific types of road injuries (RIs) in the previous Global burden of disease (GBD) studies is lacking. The present work aimed to analyze the burden of three common RIs using the updated data of the GBD 2019, which would inform policy-making.

METHODS

Data on cyclist road injuries (CRIs), motorcyclist road injuries (MRIs), and motor vehicle road injuries (MVRIs) were extracted from the GBD 2019. Trends of age-standardized rate (ASR) were predicted using estimated annual percentage change (EAPC) from 1990 to 2019.

RESULTS

Over the past three decades, the global incident ASRs of CRIs and MRIs presented increasing trends, but that of MVRIs declined slightly. However, trends of death and disability adjusted life years (DALYs) caused by three common RIs decreased in most regions and countries. Particularly, trends in ASRs of years of life lost (YLLs) cuased by RIs decreased more pronouncedly than that of years of life lived with disability (YLDs). The burden of three common RIs showed significant social and demographic characteristics. Low-middle and middle socio-demographic index (SDI) areas had a heavy burden of RIs, particularly CRIs and MRIs. However, the high SDI area undertook a relatively low burden, and presented more pronounced downward trends in death and DALYs.

CONCLUSIONS

The burden and changing trends of three common RIs were geographically heterogeneous. The findings highlighted that increasing incident trends of RIs needed more cost-effective measures of prevention and intervention.

A full Bayesian multilevel approach for modeling interaction effects in single-vehicle crashes

Interaction effects constitute crucial crash attributes that can be classified into two distinct categories: spatiotemporal interactions and factor interactions. These interactions are rarely addressed systematically in modeling the severity of single-vehicle (SV) crashes. This study focuses on uncovering these crash attributes by designing a full Bayesian spatiotemporal interaction multilevel logit (STIML-logit) approach with heterogeneity in means and variances (HMV). Meanwhile, a nested Gaussian conditional autoregressive (CAR) structure is proposed to fit the spatiotemporal interaction component and its effectiveness is verified by calibrating four different interaction patterns. A standard multilevel logit (with and without HMV), a multilevel logit with HMV, and a spatiotemporal multilevel logit with HMV are constructed for comparison. Risk factors are decomposed into traffic environment factors (group level) and individual crash factors (case level) to construct a multilevel structure and to capture possible interactions between risk factors from different levels (cross-level factor interactions). We perform regression modeling utilizing SV crash cases covering 96 major urban roads in Shandong, China. The modeling results underscore several significant findings: (1) the STIML-logit with HMV demonstrates the best regression performance, suggesting that systematically dealing with the interaction effects and the HMV is a trustworthy modeling perspective; (2) crash models with the nested CAR outperform those with the traditional CAR and the result is supported by all the spatiotemporal statistical functions, highlighting the potential advantages of the nested structure; (3) all the environment factors maintain significant interactions with the case factors, highlighting that the contribution of the environment factors to crash injuries is not constant but is rather influenced by the specific case-related crash factors. The study introduces a promising regression architecture for modeling crash injuries and revealing subtle crash attributes.

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.

Counterfactual safety benefits quantification method for en-route driving behavior interventions

Driving behavior intervention is a dominant traffic safety countermeasure being implemented that has substantially reduced crash occurrence. However, during implementation, the intervention strategy faces the curse of dimensionality as there are multiple candidate intervention locations with various intervention measures and options. Quantifying the interventions' safety benefits and further implementing the most effective ones could avoid too frequent interventions which may lead to counterproductive safety impacts. Traditional intervention effects quantification approaches rely on observational data, thus failing to control confounding variables and leading to biased results. In this study, a counterfactual safety benefits quantification method for en-route driving behavior interventions was proposed. Empirical data from online ride-hailing services were employed to quantify the safety benefits of en-route safety broadcasting to speed maintenance behavior. Specifically, to effectively control the impacts of confounding variables on the quantification results of interventions, the "if without intervention" case of the intervention case is inferred based on the structural causality model according to the Theory of Planned Behavior (TPB). Then, a safety benefits quantification method based on Extreme Value Theory (EVT) was developed to connect changes of speed maintenance behavior with crash occurrence probabilities. Furthermore, a closed-loop evaluation and optimization framework for the various behavior interventions was established and applied to a subset of Didi's online ride-hailing service drivers (more than 1.35 million). Analyses results indicated safety broadcasting could effectively reduce driving speed by approximately 6.30 km/h and contribute to an approximate 40% reduction in speeding-related crashes. Besides, empirical application results showed that the whole framework contributed to a remarkable reduction in the fatality rate per 100 million km, from an average of 0.368 to 0.225. Finally, directions for future research in terms of data, counterfactual inference methodology, and research subjects have been discussed.

Modelling injury severity in single-vehicle crashes using full Bayesian random parameters multinomial approach

Single-vehicle (SV) crash severity model considering spatiotemporal correlations has been extensively investigated, but spatiotemporal interactions have not received sufficient attention. This research is dedicated to propose a superior spatiotemporal interaction correlated random parameters logit approach with heterogeneity in means and variances (STICRP-logit-HMV) for systematically characterizing unobserved heterogeneity, spatiotemporal correlations, and spatiotemporal interactions. Four flexible interaction formulations are developed to uncover the spatiotemporal interactions, including linear structure, Kronecker product, mixture-2 model, and mixture-5 model. Four candidate approaches-random parameters logit (RP-logit), RP-logit with heterogeneity in means and variances (RP-logit-HMV), correlated RP-logit-HMV (CRP-logit-HMV), and spatiotemporal CRP-logit-HMV (STCRP-logit-HMV)-are also established and compared with the proposed model. SV crash observations in Shandong Province, China, are employed to calibrate regression parameters. The model comparison results show that (1) the performance of the RP-logit-HMV model outperforms the RP-logit model, implying that capturing heterogeneity in the means and variances can strengthen model fit; (2) the CRP-logit-HMV model and the RP-logit-HMV model are comparable; (3) the STCRP-logit-HMV model outperforms the CRP-logit-HMV model, implying that addressing the spatiotemporal crash mechanisms is beneficial to the overall fitting of the crash model; (4) the STICRP-logit-HMV model performs better than the STCRP-logit-HMV model and this finding remains stable across different interaction formulations, indicating that comprehensively reflecting the spatiotemporal correlations and their interactions is a promising approach to model SV crashes. Among the four interaction models, the STICRP-logit-HMV model with mixture-5 component maintains the best fit, which is a recommended approach to model crash severity. The regression coefficients for young driver, male driver, and non-dry road surface are random across observations, suggesting that the influence of these factors on SV crash severity maintains significant heterogeneity effects. The research results provide transportation professionals with a superior statistical framework for diagnosing crash severity, which is beneficial for improving traffic safety.