Prediction Mortality Rate Due to the Road-Traffic Accidents in Kazakhstan

  • Nurbek IGISSINOV 1. Department of Surgical Diseases № 2, Astana Medical University, Nur-Sultan, Kazakhstan 2. Department of Science and Analytic, International High School of Medicine, Bishkek, Kyrgyzstan 3. Central Asian Cancer Institute, Nur-Sultan, Kazakhstan 4. Eurasian Institute for Cancer Research, Bishkek, Kyrgyzstan
  • Alma AUBAKIROVA Republican Center for Health Development, Nur-Sultan, Kazakhstan
  • Galiya ORAZOVA Department of Public Health, Astana Medical University, Nur-Sultan, Kazakhstan
  • Gulnur AKPOLATOVA 1. Central Asian Cancer Institute, Nur-Sultan, Kazakhstan 2. Department of General and Clinical Pharmacology, Astana Medical University, Nur-Sultan, Kazakhstan
  • Saltanat URAZOVA Department of General Medical Practice № 2, Astana Medical University, Nur-Sultan, Kazakhstan
  • Dinar Tarzhanova 1. Central Asian Cancer Institute, Nur-Sultan, Kazakhstan 2. Department of General and Clinical Pharmacology, Astana Medical University, Nur-Sultan, Kazakhstan
  • Akmaral ZHANTUREYEVA 1. Central Asian Cancer Institute, Nur-Sultan, Kazakhstan 2. Department of General and Clinical Pharmacology, Astana Medical University, Nur-Sultan, Kazakhstan
  • Yerlan KUANDYKOV Department of Family Medicine, South Kazakhstan Medical Academy, Shymkent, Kazakhstan
Road-traffic accidents, Mortality, Prediction, Kazakhstan


Background: As a result of the road traffic accidents 1.25 mln. of working-age people die each year on the roads. Frequency of the RTA is 11 times higher in our country than in Europe, that influence on demographic and economic situation in the republic. Creation of the math modeling and prediction of traffic mortality rate in Kazakhstan will allow to develop measure on its decrease.

Methods: Short-term dotted prediction of population mortality level of Kazakhstan was used, in particular – methods of regressive analysis. General prognosis throughout the country up to 2021 was made on the basis of data for 1999-2018. The more relevant method for prediction is exponential function taking into account the features of mortality rate level trend.

Results: Prediction of traffic fatalities without division into the age-related groups for 2019 is 2132±181 case with a probability 2/3. Expected levels for 2020-2027 cases, for 2021-1927 cases.

Annual mortality decrease rate according to the 0-19 age-related at an average is 6.4% among men and 5.8% among women, according to age group as a whole – by 6.2%; from 20 up to 64 age related group – 5.1 % on all population category; older 65 age –group is by 2.2 %, 3.7 % among men, 2.9% among women as a whole.

Conclusion: In the foreseeable future the number of traffic deaths in Kazakhstan will tend to decrease at a slower pace. Mortality rates due to road traffic accidents among working-age men will be 3 times higher than women in this age group.


1. World Health Organization (2015). Global status report on road safety 2015. 340 p.
2. World Health Organization (2008). Global burden of disease: 2004 update. 160 p.
3. World Health Organization (2018). Global status report on road safety 2018. 424 p.
4. Buleshova AM, Buleshov MA, Kudryavtsev V et al. (2016) Jepidemiologija travmatiz-ma v g. Shymkent Juzhno-Kazahstanskoj oblasti Respubliki Kazahstan: obosno-vanie neobhodimosti sozdanija munici-pal''nogo registra travm (Epidemiology of injuries in Shymkent, Southern Ka-zakhstan: justification for the need for es-tablishment of municipal injury registry) [in Russian]. Jekologija Cheloveka, 6: 55-61.
5. Law of the Republic of Kazakhstan dated July 4, 2008 55-IV “On Amendments and Additions to Certain Legislative Acts of the Republic of Kazakhstan on Road Safety” [in Russian].
6. Aubakirova AS, Kim SV, Igisinov NS (2012). Trendy vozrastnyh pokazatelej smertnosti ot dorozhno-transportnyh proisshestvij v Kazahstane (Тrends of age indicators of mortality from road traffic accidents in Kazakhstan) [in Rus-sian]. Klinicheskaja medicina Kazahstana. 1 (24): 17-19.
7. Aubakirova A, Kossumov A, Igissinov N (2013). Road traffic accidents in Kazakh-stan. Iran J Public Health, 42 (3): 231-239.
8. Koornstra MJ (2007). Prediction of traffic fatalities and prospects for mobility be-coming sustainable-safe. Sadhana, 32 (4): 365-395.
9. Sharma B, Katiyar VK, Kumar K (2016). Traffic accident prediction model using support vector machines with Gaussian kernel. Proceedings of Fifth International Confer-ence on Soft Computing for Problem Solving: Ad-vances in Intelligent Systems and Computing, vol 437. Springer, Singapore: 1-10.
10. Gardner Jr ES (1985). Exponential smooth-ing: The state of the art. Journal of Forecast-ing, 4 (1): 1-28.
11. Feng L, Shi Y (2018). Forecasting mortality rates: multivariate or univariate models? Journal of Population Research, 35 (3): 289-318.
12. Sethi D, Racioppi F, Mitis F & World Health Organization (2007). Youth and road safety in Europe: policy briefing, 34 р.
13. Makridakis S, Andersen A, Carbone R, Fil-des R et al (1982). The accuracy of ex-trapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting, 1(2): 111-153.
14. Haddon W Jr (1968). The changing ap-proach to the epidemiology, prevention, and amelioration of trauma: the transition to approaches etiologically rather than descriptively based. Am J Public Health Na-tions Health, 58(8): 1431-1438.
15. Mitchell D, Brockett P, Mendoza-Arriaga R, Muthuraman K (2013). Modeling and forecasting mortality rates. Insurance: Math-ematics and Economics, 52 (2): 275-285.
16. Renshaw AE, Haberman S (2003). Lee-Carter mortality forecasting with age-specific enhancement. Insurance: Mathemat-ics and Economics, 33 (2): 255-272.
17. Koyama T, Matsumoto K, Okuno T, Domen K (2005). A new method for predicting functional recovery of stroke patients with hemiplegia: logarithmic modelling. Clin Rehabil, 19 (7): 779-789.
18. Fulton L, Lasdon LS, McDaniel RR (2007). Cost drivers and resource allocation in military health care systems. Mil Med, 172 (3): 244-249.
19. Orazova G, Karp L, Rakhimbekova G, Nogayeva A (2016). Mathematical model-ing and forecasting of esophageal and stomach cancer case rate in Kazakhstan. J Clin Med Kaz, 2 (40): 43-49.
20. Razzaghi A, Bahrampour A, Baneshi MR, Zolala F (2013). Assessment of trend and seasonality in road accident data: an Ira-nian case study. Int J Health Policy Manag, 1 (1): 51-55.
21. Yousefzadeh-Chabok S, Ranjbar-Taklimie F, Malekpouri R, Razzaghi A (2016). A time series model for assessing the trend and forecasting the road traffic accident mor-tality. Arch Trauma Res, 5 (3): e36570.
22. Zolala F, Haghdoost AA, Ahmadijouybari T, et al (2016). Forecasting the trend of traf-fic accident mortality in West Iran. Health Scope, 5 (3): e31336.
23. Zhang X, Pang Y, Cui M, Stallones L, Xiang H (2015). Forecasting mortality of road traffic injuries in China using seasonal au-toregressive integrated moving average
How to Cite
IGISSINOV N, AUBAKIROVA A, ORAZOVA G, AKPOLATOVA G, URAZOVA S, Tarzhanova D, ZHANTUREYEVA A, KUANDYKOV Y. Prediction Mortality Rate Due to the Road-Traffic Accidents in Kazakhstan. Iran J Public Health. 49(1):68-76.
Original Article(s)