Archives of Academic Emergency Medicine. 2019; 7 (1):e38 OR I G I N A L RE S E A RC H Factors with the Highest Impact on Road Traffic Deaths in Iran; an Ecological Study Alireza Razzaghi1, Hamid Soori2∗, Amir Kavousi3, Alireza Abadi4,5, Ardeshir Khosravi6 1. Safety Promotion and Injury Prevention Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 2. Safety Promotion and Injury Prevention Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 3. Safety Promotion and Injury Prevention Research Center, Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 4. Department of Community Medicine, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 5. Social Department of Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran 6. Department of Statistics and Informatics, Iranian Ministry of Health and Medical Education, Tehran, Iran. Received: April 2019; Accepted: June 2019; Published online: 16 July 2019 Abstract: Introduction: The largest proportion of road traffic deaths (RTDs) happen in Low and Middle Income Countries (LMICs). The efforts for decreasing RTDs can be successful if there is precise information about its related risk factors. This study aimed to determine economic, population, road, and vehicle factors with the highest impacts on RTDs in Iran. Methods: This is an ecological study, which has been done using covariates including: the pop- ulation density, economic growth, urbanization, distance traveled (km) in 100 thousand people, the length of urban roads, the length of rural roads and the Vehicle per 1000 population for each province of Iran in 2015. The covariates considered had been gathered from different sources and to determine which one of the covariates has an effect on RTDs, the Negative Binomial (NB) regression model was used. Results: The mean number of RTDs per 100000 population was 474 ± 70.59 in 2015. The highest and lowest rates of death belonged to Fars and Qom provinces, respectively. The results of the univariate model showed the population density as the only covariate of RTDs (p=0.001). Also, among other covariates, GDP was the only variable with a p-value equal to 0.2. In the multivariate NB model, it was seen that the population density (p=0.001), and GDP (p=0.02) signifi- cantly correlated with RTDs. For a unit (Million Rial) increase in the GDP of the province, the number of deaths decreased by as much as 0.0014. In addition, for a unit increase in population density, the number of deaths went up by as much as 30. Conclusion: Population density and GDP had positive and negative effects on the number of fatal road traffic injuries, respectively. By considering these factors in presentational and controlling programs on road traffic injuries, it is possible to decrease the RTDs. Keywords: Death; accidents, traffic; mortality; multiple trauma Cite this article as: Razzaghi A, Soori H, Kavousi A, Abadi A, Khosravi A. Factors with the Highest Impact on Road Traffic Deaths in Iran; an Ecological Study. Arch Acad Emerg Med. 2019; 7(1): e38. 1. Introduction Road traffic crashes (RTCs) are one of the main causes of death in all ages, especially among the 15-29 year-old peo- ple all over the world. The cost of RTCs is approximately 3% of Gross Domestic Product (GDP), which rises to 5% in Low and Middle Income Countries (LMICs). The increasing trend ∗Corresponding Author: Hamid Soori; Safety Promotion and Injury Preven- tion Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Email: hsoori@yahoo.com,Tel: +982122439980 of RTCs is higher among the countries, which experience the rapid growth of population, urbanization, and motorization (1, 2). A high proportion of road traffic deaths (RTDs) hap- pen in LMICs. There is a rapid increase in income and eco- nomic development in LMICs, which causes rapid change and motorization. However, the issues of road safety, re- lated infrastructure development, safety improvement of ve- hicles, and changing the effective policies are not in accor- dance with economic changes, urbanization, and motoriza- tion, which leads to manifestation of many problems related to road safety (1, 3). This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem A. Razzaghi et al. 2 In high-income countries, road safety system has managed well in accordance with motorization and economic growth. In these countries, actions such as developing the safer roads, safer vehicles, and effective road safety management system have led to a significant reduction in RTDs (1, 2). Ac- cording to international reports, for the first time in history, the global urban living population exceeded 50% of the total population in 2007 and it is constantly rising. By 2020, about 70% of the world population will live in urban areas (4). The world is rapidly urbanizing with extensive changes in popu- lation, sustainable mobility feature and the effects of intelli- gent electronic systems on road safety (1, 4). In some coun- tries such as Iran, the rate of urbanization is higher than eco- nomic growth (5). However, effective efforts have not been made to improve safety in the road network and vehicles, in spite of increasing the number of vehicles and the length of roads (1, 6). So in some provinces in Iran, the large number of vehicles and increasing travels in and out of cities make them prone to RTCs (7). In Iran, evaluating the condition of road safety, using the Road Safety Development Index (RSDI), showed that they are not in a good condition regarding road network safety despite little improvements (8). Meanwhile, road network is used for more than 90% of total national shippings in Iran (9). It is expected that RTDs will impose a heavy cost on commu- nities if effective efforts are not made (10). The governments can make effective efforts to decrease RTCs only if there are valid and reliable data regarding road traffic injuries and deaths (11). In many LMICs, there are no accurate epidemio- logic data on RTCs. Using statistical methods can be helpful for determining the factors affecting injuries or deaths (12, 13). According to global status report on road safety 2018, the estimated rate of road traffic deaths per 100000 popula- tions is 20.5 around the world (1). There is no information on the risk factors of RTD in Iran. This study was conducted to determine factors with the highest impacts on RTDs in economic, population, urbanization, length of roads, and ve- hicle per 1000 population categories using count regression models. 2. Methods 2.1. Study design and setting This is an ecological study, which was carried out with the aim of modeling RTDs by studying population density, eco- nomic growth, urbanization rate, number of travels, the length of roads, and the number of the vehicles per 1000 pop- ulation as covariates in all provinces (31 provinces) of Iran using the data of 2015. 2.2. Data gathering In this study, the considered covariates were gathered from different sources. The statistics of RTDs, as a dependent variable in count regression models, were obtained from the Ministry of Health and Medical Education (MOHME). In Iran, registering and collecting the vital data is done by dif- ferent organizations such as; the National Organization for Civil Registration (NOCR) (as the governmental system that records the vital events), the Iranian forensic Medicine Orga- nization (as a reference point for unnatural deaths), the med- ical council (as a non-governmental organization for regis- tering all health care professionals), municipalities (as an or- ganization responsible for cemetery in rural and urban ar- eas), and ministry of health and medical education (14). The national reports of World Health Organization in RTDs are prepared by forensic medicine organization (1, 2). However, MOHME is the only registration system, which is based on the International Classification of Disease (ICD) standards. According to the findings of a study in 2009 in Iran, the cov- erage rate of MOHME registration system is nearly complete (15). The explanatory variables in this study included: urbaniza- tion rate in each province (percent), road length (km), Gross domestic product (GDP) (as an economic factor), population density, the number of vehicles per 1000 population, and the distance traveled per 100000 population (km). Iran is sub- divided into thirty-one provinces and all data were in the province level. The GDP information of each province was obtained from Tehran Chamber of Commerce, Industries, and Agriculture in 2011 (16). The population of provinces and their urbanization rate were gathered from population census, which has done by the statistical center of Iran (17). The distance (km) traveled per 100000 population by differ- ent vehicles was obtained from the information technology office at the Ministry of Road and Transportation (9). The number of registered vehicles was obtained from the Law En- forcement Force of Iran, statistical office (18). The length of road data in each province was obtained from the statistical center of Iran, the transportation sector statistic (17). 2.3. Statistical Analysis At first, the Poisson distribution was assessed. In the Poisson model, the mean and the variance should be equal. An over- dispersion in data was found using test of over-dispersion parameter alpha by running the same model using negative binomial distribution. The parameter alpha value equals to 0.305. This strongly suggests that alpha is non-zero and the negative binomial model is more appropriate than the Pois- son model (19). The analysis was done in two steps using uni- variate and multivariate models. The variables with a p-value of less than 0.2 in univariate analysis, entered the multivari- This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 3 Archives of Academic Emergency Medicine. 2019; 7 (1):e38 ate regression model (20). Finally, factors affecting RTDs and their effect sizes were identified. STATA software, edition 14, was used for analyzing the data. 3. Results The results of the study showed that the mean number of RTDs per 100000 population was 474 ± 70.59 in 2015. The highest and lowest rates of death per 100000 popula- tion according to Poisson model were related to Fars and Qom provinces, respectively. The highest rate of GDP be- longed to Tehran, Khuzestan, Isfahan, and Razavi Khorasan provinces. The highest and lowest traveled distances per 100000 people (km) were seen in Ilam and Qazvin provinces, respectively. The population density was highest in Tehran and lowest in Kohgiluyeh and Boyer-Ahmad provinces. The overall urbanization rate in the country (all provinces) was 69.76%. Qom, Tehran, and Alborz had the highest rates of urbanization, respectively. Also, the lowest rates of urban- ization were seen in Sistan and Baluchestan, Golestan, and Hormozgan provinces. The highest number of vehicles per 1000 people was observed in Tehran and the lowest in Sis- tan and Baluchestan. The longest rural roads belonged to Fars, Khuzestan, and Razavi Khorasan, respectively. Also, the shortest rural roads belonged to Qom, Alborz, and Ilam, re- spectively. About the urban and suburban regions, the high- est roads belonged to Sistan and Baluchestan, Fars, and Kho- rasan Razavi, respectively (table 1). The results of univariate analysis using negative binomial model is shown in table 2. The results of the univariate model showed population density as the only covariate of RTDs (p=0.001). Also, among other covariates, GDP was the only variable with a p-value equal to 0.2. So, population density and GDP were the covariates selected to enter mul- tivariate negative binomial model. In the multivariate NB model, it was seen that population density (p=0.001), and GDP (p=0.02) both significantly correlated with RTDs (table 3). The value of the β parameter for GDP was equal to - 0.0014. In other words, for a unit (Million Rial) increase in the GDP of the province, the number of deaths decreases by as much as 0.0014. Also, the value of the β parameter for the population density was equal to 30.48. This value means that, for a unit increase in population density, the number of deaths rises by as much as 30. 4. Discussion The findings of NB model showed that the effects of popu- lation density and GDP on RTD were statistically significant. Population is one of the factors affecting RTDs. In the Global Status Report on Road Safety (GSRRS) in 2018, the mortality of RTCs was estimated using NB model. In this report, the co- variate of population was introduced as an effective factor in RTCs (1). Some issues emerged following population growth such as: high density of population in cities (21), increase in the number of vehicles, changes in the population demo- graphics, and changes in transportation (22). In LMICs, there is rapid growth in urban areas regardless of related infrastruc- ture and facilities. This issue leads to manifestation of some road traffic related problems including: property damage, in- juries, and deaths (23, 24). In Iran, the population has been growing in recent decades and there has been an extensive migration from rural areas to urban areas (7). It should be noted that population growth does not cause an increase in RTCs and their related deaths in all countries. According to GSRRS of WHO in 2018, the number of deaths in Germany (with a population of 81914672), which has a population sim- ilar to Iran (with a population of 77447169), was about one fifth compared to Iran (3206 versus 15932) in 2016 (1). There- fore, Germany and Iran have a similar population, but the number of deaths is not the same. One of the reasons for this difference is discrepancy between rapid growth of pop- ulation and capacity building in the transportation system (21)(25). While in many high-income countries, the increase in population has been followed by effective changes in the transportation system. For example, the increasing trend of the population has caused the shift from private motorized transport to public transport, or has led to making the infras- tructure for cycling or walking instead of using a motorized vehicle. Moreover, the rapid changes in vehicle technologies and their improvement by applying intelligent systems have caused improvement in the safety and prevention of crashes and related deaths (26). Along with population growth, there are some changes in demographic characteristics. For exam- ple, in many countries the elderly population has an increas- ing trend. According to WHO report in 2015, the proportion of elderly people in Iran (people 60 years or above) will dou- ble during 2015-2030 (27). The findings of other studies show that the most important injury among the elderly people is road traffic injuries, which has the highest incidence, death rate and Disability Adjusted Life Years (DALY) among them (28). GDP (as an economic factor) was the second factor affect- ing RTDs in this study. Most studies in this area have used economic indicators such as GDP (29). It was shown that with a raise in GDP, the number of deaths has decreased in provinces. The findings of earlier studies showed that road traffic deaths will increase with launch of development. The rate of RTDs will begin to decrease when exceeding a thresh- old level in economic status (30, 31). In the early stages, eco- nomic growth leads to an increase in vehicles and this con- dition leads to increase in their related injuries and deaths. This is more important for LMICs, which are mostly in the early stages of economic developing. The findings of a study in 1975-1988 showed that the rate of road traffic deaths in This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem A. Razzaghi et al. 4 Table 1: Distribution of the studied variables among provinces Province GDP (Mil- lion Rials) Traveling (km) per 100000 population Population density (2015) Urbanization rate (Per- cent) Number of RTD RTD rate per 100000 population (Adjusted) Vehicle per 1000 popu- lation Rural Roads (Km) Urban and Suburban Roads (Km) Country 6225659738 59.55 1.00 69.76 14716 - 19.22 - - East Azer- baijan 207139439 42.61 0.0490 71.9 1007 26.26 10.96 6203 3464 West Azer- baijan 125717289 42.70 0.0408 65.4 727 22.42 12.42 5107 2948 Ardabil 57913670 58.29 0.0160 68.2 179 13.92 11.21 3711 1599 Isfahan 85400000 71.83 0.0642 88 723 14.31 21.06 4517 5414 Alborz 416864342 43.01 0.0335 92.6 252 9.71 3.95 907 393 Ilam 67161398 214.08 0.0072 68.1 155 26.67 14.41 1425 1482 Bushehr 212663477 66.10 0.0143 71.9 309 27.51 27.63 1894 2121 Tehran 1436431500 69.59 0.1652 93.9 233 11.83 34.37 1589 983 Chaharmahal and Bakhtiari 40099640 55.87 0.0118 64.1 182 19.50 13.95 1667 1296 South Kho- rasan 28958350 116.42 0.0096 59 198 25.74 11.86 6686 4455 Razavi Kho- rasan 331292272 97.58 0.0803 73.1 1598 25.15 19.60 7044 6412 North Kho- rasan 34956332 59.83 0.0109 56.1 190 20.90 16.11 2322 1359 Khuzestan 836240240 40.66 0.0592 75.5 822 17.16 20.23 8477 5276 Zanjan 52830070 34.12 0.0132 67.3 207 19.56 14.03 3590 1658 Semnan 55759341 58.66 0.0087 79.8 161 23.92 15.25 1434 1600 Sistan and Baluches- tan 75230327 63.88 0.0345 48.5 741 26.54 9.69 7401 8010 Fars 262027801 69.99 0.0607 70.1 1608 33.62 22.20 8522 7430 Qazvin 84992827 30.42 0.0159 74.8 261 20.88 13.93 3239 1341 Qom 59519554 56.05 0.0159 95.2 54 4.37 18.91 703 678 Kurdistan 60784463 44.87 0.0200 70.8 428 27.88 9.83 4218 1819 Kerman 164052960 40.58 0.0395 58.7 852 27.33 16.67 6494 5692 Kermanshah 106086048 51.30 0.0247 75.2 484 24.48 11.49 4364 2841 Kohgiluyeh and Boyer- Ahmad 143413674 31.90 0.0088 55.7 116 16.50 11.31 2868 1512 Golestan 70512931 48.59 0.0234 53.3 444 23.33 16.06 3103 1231 Gilan 126890610 42.69 0.0319 63.3 355 13.95 13.27 6445 1891 Lorestan 70281385 50.54 0.0222 64.5 360 19.82 14.41 5177 1856 Mazandaran 202791471 33.23 0.0410 57.8 773 24.29 14.86 4769 2332 Markazi 125424307 42.35 0.0180 76.9 270 18.36 16.03 3608 1845 Hormozgan 132781740 43.74 0.0219 54.7 420 24.56 23.06 5341 2897 Hamadan 88881887 61.07 0.0220 63.1 433 24.12 16.01 2991 2036 Yazd 108536644 63.60 0.0140 85.3 174 15.97 31.04 2580 2295 RTD: Road Traffic Deaths Malaysia and Colombia had increased; yet, it had decreased in high-income countries by as much as 25-50% (32). The results of a study in Brazil in 2008 showed that in the pre- vious decade the north and north-east areas with low GDP had higher rates of death in comparison with other areas with high GDP (33). In Iran, the high rate of road traffic deaths correlates with the number of vehicles. The number of vehicles has raised fol- lowing economic growth, which is in its early stage in Iran (5). According to reports of statistical center of Iran, the rate of motorization (the number of vehicles per 1000 population) was higher than the economic growth rate during the years 1971-2009 (5). In low and middle-income countries, failure to balance economic growth with the motorization can play a This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 5 Archives of Academic Emergency Medicine. 2019; 7 (1):e38 Table 2: The results of univariate analysis using the Negative binomial regression model Variables Coefficient SE P-value 95% CI Low Upper Population Ratio 22.06 5.45 0.001 11.37 32.75 Urbanization Ratio -0.01 0.012 0.3 -0.034 0.013 Gross Domestic Product 0.0007 0.00077 0.2 -0.0006 0.002 Vehicle1 0.013 0.024 0.5 -0.03 0.06 Urban Road2 0.0002 0.00004 0.7 -0.03 0.003 Rural Road3 0.0002 0.00003 0.6 -0.002 0.0003 Passenger traveler4 -0.0007 0.005 0.8 -0.01 0.009 1. Vehicle per 1000 population, 2 Urban Road (km) in each Province,3 Rural road (km) in each Province, 4 adjusted per/for population. SE: standard error, CI: confidence interval. Table 3: The results of multivariate analysis of Negative binomial model Variables Coefficient SE P-value 95% CI Low Upper Population Ratio 30.48 6.36 0.001 17.48 43.48 Gross Domestic Product -0.0014 0.0006 0.02 -0.0026 -0.00019 Constant 4.32 0.19 0.001 4.95 5.70 In alpha -1.18 0.24 - - - Alpha 0.305 0.074 - - - SE: standard error, CI: confidence interval. role in increasing the incidence of road traffic crashes. How- ever, in Iran the rate of road traffic deaths has had a decreas- ing trend in 2007-2018 (34). So the decreasing effect of GDP on RTCs can be explained considering that economic growth has led to an improvement in road safety and raise in invest- ment in transport infrastructure and this has ultimately led to a reduction in RTDs (5, 35). 5. Strengths and Limitations One of the strengths of this study is that it was implemented at the national level and included provincial comparison. On the other hand, this is an ecological study and this should be noted in the interpretation of results. In an ecological study, the ecological inference fallacy occurs if this will not be con- sidered in the interpretation of ecological level data to the in- dividual level. One of the limitations of this study could be the possible information bias in RTD data obtained from the MOHME. 6. Conclusion The covariate population density increases the fatal road traffic injuries and Gross Domestic Production decreases that. By considering these factors in presentational and con- trolling programs done on road traffic injuries, it is possible to further decrease road traffic deaths. 7. Appendix 7.1. Acknowledgements All the people who helped us in collection of the required data are thanked for their cooperation. 7.2. Author contribution All the authors met the criteria of authorship based on the recommendations of the international committee of medical journal editors. Authors ORCIDs Alireza Razzaghi: 0000-0003-1874-6364 Hamid Soori: 0000-0002-3775-1831 Amir Kavousi: 0000-0003-3922-0564 Ardeshir Khosravi: 0000-0003-2963-0674 7.3. Funding/Support This article is derived from a PhD thesis funded by Shahid Be- heshti University of Medical Sciences and Iran National Sci- ence Foundation (INSF). 7.4. Conflict of interest The authors declare that there is no conflict of interest re- garding the present study. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem A. Razzaghi et al. 6 References 1. World Health Organization. Global status report on road safety 2018. Geneva, Switzerland. 2. World Health Organization. Global status report on road safety 2015. Geneva, Switzerland. 3. Road Deaths and Injuries Hold Back Economic Growth in Developing Countries: The World Bank2018. 4. City BL, Assessment E. Urbanization and health. 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Downloaded from: http://journals.sbmu.ac.ir/aaem 7 Archives of Academic Emergency Medicine. 2019; 7 (1):e38 development and traffic accident mortality in the indus- trialized world, 1962–1990. International journal of epi- demiology. 2000;29(3):503-9. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem Introduction Methods Results Discussion Strengths and Limitations Conclusion Appendix References