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A study using HGLM on regional difference of the dead due to injuries  

Kim, Kil-Hun (Department of Statistics, Pukyong National University)
Noh, Maeng-Seok (Department of Statistics, Pukyong National University)
Ha, Il-Do (Department of Asset Management, Daegu Haany University)
Publication Information
Journal of the Korean Data and Information Science Society / v.22, no.2, 2011 , pp. 137-148 More about this Journal
Abstract
In this paper, we systematically investigate regional differences of the dead due to injuries in cities, towns and counties about transportation accidents, suicides and fall accidents, which have recently been an important issue of health problems in Korea, The data are from the Annual Report on the Cause of Death Statistics in Korea in 2008. They include the deaths over the age 19 from transportation accidents, suicides and fall accidents with the criterion of the International Statistical Classification of Diseases. Poisson HGLM is applied to estimate the mortality rate under the assumption that the number of deaths follow a Poisson distribution, by considering regions as random effects and by adjusting age, sex and standardized residence tax as fixed effects. Using the results of random effects prediction, the regional differences in cities, counties and towns are marked in disease mapping. The results showed that there were significant regional differences of mortality rates for transportation accidents and suicides, but no significant differences for fall accidents.
Keywords
Disease mapping; hierarchical generalized linear model; injury; random effect;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 Park, K. H., Lee, J. S., Kim, Y., Kim, Y. I. and Kim, J. Y. (2009). The socioeconomic cost of injuries in South Korea. Journal of Preventive Medicine and Public Health, 42, 5-11.   DOI
2 Lee, Y. and Nelder, J. A. (2001). Hierarchical generalized linear models: A synthesis of generalized linear models, random-effect models and structured dispersions. Biometrika, 88, 987-1006.   DOI   ScienceOn
3 Lee, Y., Nelder, J. A and Pawitan (2006). Generalized linear models with random effects: Unified analysis via h-likelihood, Chapman and Hall, London.
4 OECD. (2007). Organization for economic cooperation and development, OECD Health Data.
5 Park, E., Hyun, M., Lee, C., Lee, E. and Hong, S. (2007). A Study on regional differentials in death caused by suicide in South Korea. Journal of Korean Academy of Public Health Nursing, 37, 44-51.   DOI
6 Park, J. T. and Lee, S. E. (2001). A comparative study of small area estimation methods. Journal of the Korea Data and Information Science Society, 12, 47-55.
7 Park, J. S., Lee, J. Y. and Kim, S. D. (2003). A study for the effects of economic growth rate and unemployment rate to suicide rate in Korea. The Korean Journal of Preventive Medicine, 36, 85-91.   DOI   ScienceOn
8 Kim, Y. H. and Kim, K. S. (2009). Small area estimation of the insurance benefit for customer segmentations. Journal of the Korean Data & Information Science Society, 20, 77-87.
9 Kim, Y. W. and Cho, N. K. (2004). HGLM and EB estimation methods for disease mapping. The Korean Journal of Applied Statistics, 17, 431-443.   DOI
10 Kim, Y. W. and Sung, N. Y. (2000). Application of in-direct estimation for small area statistics. Journal of the Korean Data & Information Science Society, 11, 111-126.
11 Lee, Y. and Ha, I. D. (2010). Orthodox BLUP versus h-likelihood methods for inferences about random effects in tweedie mixed models. Statistics and Computing, 20, 295-303   DOI   ScienceOn
12 Lee, Y., Jang, M. and Lee, W. (2011). Prediction interval for disease mapping using hierarchical likelihood. Computational Statistics, 26, 159-179.   DOI   ScienceOn
13 Lee, Y. and Nelder, J. A. (1996). Hierarchical generalized linear models(with discussion). Journal of the Royal Statistical Society B, 58, 619-678.
14 조우현, 정우진, 임승지, 이선미, 전병찬, 김세희, 김재윤, 김지만 (2009). <우리나라 손상(injury) 폐해 감소전략 개발을 위한 사회경제적 비용 추계>, 보건복지가족부.
15 통계청 (1999-2008). <사망원인통계연보>.
16 Banerjee, S., Carlin, B.P. and Gelfand, A.E. (2004). Hierarchical modelling and analysis for spatial data, Chapman and Hall, London.
17 Clayton, D. G. and Kaldor, J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43, 671-681.   DOI   ScienceOn
18 Consumer Safety Department (2007). A survey on the safety accidents of elderly, Korea Consumer Agency, Seoul.
19 Ghosh, M., Natarajan, K., Stroud, T. W. F. and Carlin, B. P. (1998). Generalized linear models for small-area estimation. Journal of the American Statistical Association, 93, 273-282.   DOI   ScienceOn
20 Ha, I. D. and Cho, G. H. (2001). Second-order REML for random effects models. Journal of the Korean Data and Information Science Society, 12, 19-25.
21 도로교통 안전관리공단 (2002). <교통사고 통계분석 보고서>, 61-89.