Mortality Rate Model due to Transportation Accidents in Thailand
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Keywords:
mortality rate multivariate linear regression transportation accidents
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Abstract
The mortality from transportation accidents is a major problem that leads to loss of human lives and property. The deaths as a result of transportation accidents are now accepted to be a global phenomenon in virtually all countries concerned about the growth in the number of people killed. The objective was to model and forecast the transportation accident mortality rate in Thailand using death certificate reports.
A retrospective analysis of the transportation accident mortality rate was conducted in this study. This study is based on the records of the national vital registration database for the 10-year period from 2000 to 2009, provided by the Ministry of the Interior and coded as cause-of-death using ICD-10 by the Ministry of Public Health. Multivariate linear regression was used for modeling and forecasting age-specific transportation accident mortality rates in Thailand.
The transportation accident mortality increased higher in males than females. The highest was in males aged 20-29 years. The trend slightly decreases in all other ages. Having a model that provides such forecasts of transportation accident fatalities, even if based purely on statistical data analysis, can provide a useful basis for allocation of resources for transportation accident fatality prevention.
A retrospective analysis of the transportation accident mortality rate was conducted in this study. This study is based on the records of the national vital registration database for the 10-year period from 2000 to 2009, provided by the Ministry of the Interior and coded as cause-of-death using ICD-10 by the Ministry of Public Health. Multivariate linear regression was used for modeling and forecasting age-specific transportation accident mortality rates in Thailand.
The transportation accident mortality increased higher in males than females. The highest was in males aged 20-29 years. The trend slightly decreases in all other ages. Having a model that provides such forecasts of transportation accident fatalities, even if based purely on statistical data analysis, can provide a useful basis for allocation of resources for transportation accident fatality prevention.
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How to Cite
Sriwattanapongse, W., Prasitwattanaseree, S., Khanabsakdi, S., & Wongtra-ngan, S. (2013). Mortality Rate Model due to Transportation Accidents in Thailand. Science, Engineering and Health Studies, 7(1), 9–18. https://doi.org/10.14456/sustj.2013.1
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Research Articles
References
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Butt, Z. and Haberman, S. ( 2009). A Collection of R Functions for Fitting a Class of Lee-Carter Mortality Models using Iterative Fitting Algorithms, Sir John Cass Business School, London (UK).
Congdon, P. (2006). A model framework for mortality and health data classified by age, area, and time. Biometrics, 62: 269-278.
Jonesa, A. P., Haynesa, R., Kennedy, V., Harveyb, I. M, Jewellc, T., and Lead,. D. (2008) Geographical variations in mortality and morbidity from road traffic accidents in England and Wales. Health Place, 14(3): 519-35.
Kardara, M. and Kondakis, X. (1997). Road traffic accidents in Greece: Recent trends (1981-1991). European Journal of Epidemiology, 13(7): 765-70.
Lee, R. D. and Carter, L. R. (1992). Modeling and Forecasting U.S. Mortality. American Statistics Association, 87(419): 659-671.
Lee, R. D. and Miller, T. (2001). Evaluating the Performance of the Lee-Carter Method for Forecasting Mortality. Demography, 38(4): 537-49.
Lee, H. A., Stevenson, R. M., Wang, K., and Yau, K. K. (2002). Modeling young driver motor vehicle crashes: data with extra zeros. Accident Analysis and Prevention, 34(4): 515-21.
Lix, L. M., Ekuma, O., Brownell, M., and Roos, L. L. (2004). A framework for modeling differences in regional mortality over time. Journal of Epidemiology and Community Health, 58(5): 420-5.
Lord, D. (2006). Modeling motor vehicle crashes using Poisson-gamma models: Examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter. Accident Analysis and Prevention, 38(4): 751-66.
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Neill B. O. and Mohan, D. (2002). Reducing motor vehicle crash deaths and injuries in newly motorizing countries. British Medical Journal, 324 (7346): 1142-1145.
Pocock, J. S., Cook, G. D., and Beresford, A. S. (1981). Regression of area mortality rates on explanatory variables: What weighting is appropriate? Journal of the Royal Statistical Society, Series C Applied statistics, 30(3): 286-95.
Sriwattanapongse, W. and Khanabsakdi, S. (2011). Modeling of Mortality Rate due to Traffic Accident, Phayao Province, In Proceedings of the National Statistics and Applied Statistics Conference, Hat Yai, Songkhla, Thailand.
Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G.,Wood, A. M., and Carpenter, J. R. (2009). Multiple imputations for missing data in epidemiological and clinical research: potential and pitfalls, British Medical Journal 338:b2393, DOI: 10.1136/bmj.b2393.
Suriyawongpaisal, P. and Kanchanasut, S. (2003). Road traffic injuries in Thailand: Trends, selected underlying determinants and status of intervention. Injury Control and Safety Promotion, 10(1-2): 95-104.
Tanaboriboon, Y. (2004). ‘The Status of Road Safety in Thailand’, In the ADB-ASEAN Regional Safety Program, Country Report: CR09, Final Report, Thailand.
Tsauo, J. Y., Lee, W. C., and Wang, J. D. (1996). Age-period-cohort analysis of motor vehicle mortality in Taiwan, 1974-1992. Accident Analysis and Prevention, 28(5): 619-26.
Wikipedia (2011). [Online URL: www.en.wikipedia.org/wiki/Provinces_of_Thailand] accessed on May 20, 2012.
World Health Organization (2008). World Health Statistics, World Health Organization, Geneva. [Online URL: www.who.int/whosis/whostat/EN_WHS08_Full.pdf] accessed on June 10, 2012.
Yang, L., Lam, T. L., Lui, Y., Geng, K. W., and Lui, C. D. (2005). Epidemiological profile of mortality due to injuries in three cities in the Guangxi, China. Accident Analysis and Prevention, 37(1): 137-141.
Butt, Z. and Haberman, S. ( 2009). A Collection of R Functions for Fitting a Class of Lee-Carter Mortality Models using Iterative Fitting Algorithms, Sir John Cass Business School, London (UK).
Congdon, P. (2006). A model framework for mortality and health data classified by age, area, and time. Biometrics, 62: 269-278.
Jonesa, A. P., Haynesa, R., Kennedy, V., Harveyb, I. M, Jewellc, T., and Lead,. D. (2008) Geographical variations in mortality and morbidity from road traffic accidents in England and Wales. Health Place, 14(3): 519-35.
Kardara, M. and Kondakis, X. (1997). Road traffic accidents in Greece: Recent trends (1981-1991). European Journal of Epidemiology, 13(7): 765-70.
Lee, R. D. and Carter, L. R. (1992). Modeling and Forecasting U.S. Mortality. American Statistics Association, 87(419): 659-671.
Lee, R. D. and Miller, T. (2001). Evaluating the Performance of the Lee-Carter Method for Forecasting Mortality. Demography, 38(4): 537-49.
Lee, H. A., Stevenson, R. M., Wang, K., and Yau, K. K. (2002). Modeling young driver motor vehicle crashes: data with extra zeros. Accident Analysis and Prevention, 34(4): 515-21.
Lix, L. M., Ekuma, O., Brownell, M., and Roos, L. L. (2004). A framework for modeling differences in regional mortality over time. Journal of Epidemiology and Community Health, 58(5): 420-5.
Lord, D. (2006). Modeling motor vehicle crashes using Poisson-gamma models: Examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter. Accident Analysis and Prevention, 38(4): 751-66.
Nantulya, M. V. and Reich, R. M. (2003). Equity dimensions of road traffic injuries in low and middle-income countries. Injury Control and Safety Promotion, 10(1-2): 13-20.
National Statistics Office (2010). 100th anniversary of population censuses in Thailand: Population and housing census. [Online URL: www.popcensus.nso.go.th/]
Neill B. O. and Mohan, D. (2002). Reducing motor vehicle crash deaths and injuries in newly motorizing countries. British Medical Journal, 324 (7346): 1142-1145.
Pocock, J. S., Cook, G. D., and Beresford, A. S. (1981). Regression of area mortality rates on explanatory variables: What weighting is appropriate? Journal of the Royal Statistical Society, Series C Applied statistics, 30(3): 286-95.
Sriwattanapongse, W. and Khanabsakdi, S. (2011). Modeling of Mortality Rate due to Traffic Accident, Phayao Province, In Proceedings of the National Statistics and Applied Statistics Conference, Hat Yai, Songkhla, Thailand.
Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G.,Wood, A. M., and Carpenter, J. R. (2009). Multiple imputations for missing data in epidemiological and clinical research: potential and pitfalls, British Medical Journal 338:b2393, DOI: 10.1136/bmj.b2393.
Suriyawongpaisal, P. and Kanchanasut, S. (2003). Road traffic injuries in Thailand: Trends, selected underlying determinants and status of intervention. Injury Control and Safety Promotion, 10(1-2): 95-104.
Tanaboriboon, Y. (2004). ‘The Status of Road Safety in Thailand’, In the ADB-ASEAN Regional Safety Program, Country Report: CR09, Final Report, Thailand.
Tsauo, J. Y., Lee, W. C., and Wang, J. D. (1996). Age-period-cohort analysis of motor vehicle mortality in Taiwan, 1974-1992. Accident Analysis and Prevention, 28(5): 619-26.
Wikipedia (2011). [Online URL: www.en.wikipedia.org/wiki/Provinces_of_Thailand] accessed on May 20, 2012.
World Health Organization (2008). World Health Statistics, World Health Organization, Geneva. [Online URL: www.who.int/whosis/whostat/EN_WHS08_Full.pdf] accessed on June 10, 2012.
Yang, L., Lam, T. L., Lui, Y., Geng, K. W., and Lui, C. D. (2005). Epidemiological profile of mortality due to injuries in three cities in the Guangxi, China. Accident Analysis and Prevention, 37(1): 137-141.