This study revealed that the global trends of RTDR were decreasing over a 10-year period (from 2007 to 2016). However, such trends for 25 countries out of 131 cases examined were increasing. According to the conditional LGM, the results indicated the negative effect of the HDI and its components on the intercept and the slope. Furthermore, LE was the most important HDI component, negatively with RTDR in both mean and slope models. Moreover, countries with lower slope changes in the HDI and LE mainly had a downward trend in RTDR.
In line with these results, Slaehi et al. (2019), using LGMs in 2007, 2010, and 2013 in 181 countries, had demonstrated that the HDI had a significant negative impact on RTDR2. On the other hand, although one other study had revealed that, LE, among the HDI components, had the least association with RTDR7, the time-trend analysis in the presnt study in both mean and slope models showed that the component concerned was the most important one. Another investigation had further deliberated that a decline in infant mortality rate (IMR) and a rising trend in physicians per thousand people wee significantly associated with a decline in motorcycle mortality rate as one of the worldwide health care system-related factors12. However, these factors were not assessed in the present study, considering that IMR might be an indirect indicator of LE.
Conidering Figure 2, most countries with an ascending trend in RTDR were African or Southeeast Asian ones. The results demonstrated that, among 131 countries, Egypt, Afghanistan, Lithuania, Qatar, Iran, and Slovakia had experienced the most reduction in RTDR between 2007 and 2016. On the other hand, Zimbabwe, Liberia, Central Africa, the Democratic Republic of the Congo, Malawi ,and Thailand had faced the most increasing RTDR.
With reagrd to the relationship between the HDI and RTDR (Figure 3), all countries with the HDI higher than 0.6628, except for the Kingdom of Saudi Arabia and Thailand, had experienced a diminishing trend in their RTDR in these ten years (namely, 81 countries from 83). These countries were mostly classified in very high and high HDI categories. On the other hand, countries with the HDI between 0.482 and 0.4976 and those with a mean value of HDI lower than 0.4586 (except for Ethiopia) had experienced an upward trend in RTDR (viz. 11 countries among 12). All of these countries were categorized as low HDI ones.
Regarding the relationship between the slope of the HDI and RTDR (Figure 4), countries with slopes changing less than 0.0185 had the least mean of RTDR over these ten years (namely, 87 countries out of 96 cases). Among nine misclassified countries, Thailand also had a high mean of the HDI, El Salvador and Vietnam had a medium mean, and others had a low mean of this index. While the mean value of RTDR in countries with a slope lower than 0.0185 was 15.11, the mean of RTDR in other categories was at least 21.74.
Some studies had also assessed the relationship between the HDI and RTDR6,8, but the relationship between the HDI components and RTDR had not been evaluated. Considering the relationship between the mean value of different components of the HDI and RTDR, with the model accuracy of 93.89%, the mean of LE was the most essential factor associated with RTDR. Based on this model, the mean value of LE, income, and education had influenced RTDR, respectively.
According to Figure 5, among countries with a higher mean LE, income, and education, except for the Kingdom of Saudi Arabia, 76 cases had experienced a diminishing RTD trend over these ten years. Moreover, countries in node 2 had a reduction trend of RTDR (viz. 13 countries with a mean value of RTD by 19.14). On the other hand, 23 countries with a mean LE, lower than 0.633, except for six countries named South Africa, Angola, Nigeria, Mali, Chad, and Niger, had encountered an increasing RTDR in these ten years. Thus, the policies in these six countries could be considered good examples for ones with low HDI to deal with RTD.
Besides, the present study analyzed the relationship between the rate of various components of the HDI and RTDR. In this regard, the accuracy of the model presented here was 88.55%. Considering the variable importance table, the slope of LE, education, and income was strongly correalted with RTDR, respectively. Based on Figure 6, node 1 consisted of 74 countries, except for four named El Salvador, Rwanda, Togo, the Democratic Republic of the Congo, which had a descending RTDR with a mean of 14.72. Node 2 also included seven countries, except for Timor-Leste, with a medium mean of HDI categorized in high and very high HDI countries.
Other 50 countries in nodes 3-8 had a mean value of RTD of more than 17.79. Among these nodes, nodes 3, 7, and 8 were predicted to increase RTDR in this period. Node 8 also consisted of 11 countries having a LE slope of more than 0.0305. In this node, three countries of Kazakhstan, Botswana, and South Africa had different behaviors than others and had experienced a diminishing RTDR. With an HDI mean near 0.8, Kazakhstan was categorized as a high HDI country, and the other two nations were categorized in the medium HDI.
Finally, comparing the mean and the slope of the CART models revealed that Kazakhstan and Botswana were in the first node of the mean model (with the means of LE, income, and education more than 0.633, 0.4926 and 0.6254, respectively) and in the eightth node of the slope model (LE slope more than 0.0305), facing a diminishing RTDR.
On the other hand, although Togo and the Democratic Republic of the Congo had experienced a rising RTDR in this period, in the mean model, they were in node 8 (LE<0.633, education>=0.2923, and income < 0.5823), and in the slope model, they were in the first node (with LE slope between 0.0025 and 0.0135). Although the HDI increased at this period in these two countries, they remained in the low HDI category.
Besdies, some countries had deviant behaviors compared with other nations in their own category. Among countries with very high HDI, the Kingdom of Saudi Arabia was the only case experiencing a rising RTDR in this period. In 2010, non-communicable diseases and road traffic injuries had the leading causes of disability and death in this country31, which could be explained with the suggestion by the WHO about affluent countries in the EMR, experiencing rapid economic development without sufficient investments in institutional capacities and interventions to deal with road collisions3. Moreover, people’s non-adherence to road traffic law enforcement was a crucial factor related to RTDR in this Kingdom31. However, it should be noted that transport injuries in the Kingdom of Saudi Arabia had a descending trend from third to fifth of the cause of the disability-adjusted life years (DALYs) between 2010 and 201732.
On the other hand, two deviated countries experiencing a diminishing trend in these ten years were Kazakhstan and Botswana. In this sense, Kazakhstan had promoted from a country with high HDI to a very high one over these years. Moreover, in 2008, this country had passed the Legislation of the Republic of Kazakhstan on Administrative Offences affecting people’s road traffic behaviors33. Therefore, both human development and legislative factors could be associated with a descending trend in RTD. The case of Botswana was a little different. This country had encountered its minimum RTDR in 2010, and then an increasing rate had been seen. It had also experienced one of the fastest-growing HDI and had moved up eight places from 2012 to 20174. Although the Road Traffic Act of Botswana had been passed in 2008 and a decrease in RTDR had been experienced in 2010, an increasing trend in RTD could be observed from 2013. As Mphela (2011) mentioned, the ACT had little impact on reducing RTD34. A study in this country had further demonstrated that night time travel and population density could lead to a growth in RTDR, while investing in road infrastructure could minimize it35.
Overall, the present study showed the importance of changing HDI and LE in RTDR globally. Countries with a mean of the HDI more than 0.6628 or a change in their HDI slope smaller than 0.0185 could thus reduce RTDR between 2007 and 2016. Countries with a mean LE more than 0.633 had mainly controled RTDR better than the ones with a lower index. As mentioned by the UNDP, there was a significant gap between LE at birth in 2017 among countries with different human development categories. LE at birth had been 60.8 and 69.1 years for the low and medium human development groups, resepctively. On the other hand, countries with high and very high HDI had 76 and 79.5 years of LE, respectively4. Moreover, nations with more slight changes in LE from 2007 to 2016 had better association with reduced RTDR. Based on Bishai’s hypothesis10 (mentioned earlier), it was concluded that countries with medium HDI had invested more in their high-ranked health risk factors such as infectious diseases and their nutritional status rather than road safety. Moreover, there was a lag between medical technology and dealing with road traumas in these countries. In addition to LE, Kazakhstan, the Kingdom of Saudi Arabia, and Botswana had shown the importance of sociocultural factors regarding people’s driving behaviors in mitigating RTDR. These deviated cases revealed that increasing HDI and its components and legislating law enforcement could not be sufficient factors in minimizing RTDR. Therefore, countries are suggested to implement various interventions to change drivers’ behaviors.
Among the main limitations of this study was lack of credible data at the global level (other than the HDI), which could be investigated for its association with RTDR. Furthermore, RTDR published were limited, while the prediction of RTDR in the future would be possible made through having access to more data.