DOI QR코드

DOI QR Code

Bayesian Analysis and Mapping of Elderly Korean Suicide Rates

베이지안 모형을 활용한 국내 노인 자살률 질병지도

  • Lee, Jayoun (Department of Statistics, Kyungpook National University) ;
  • Kim, Dal Ho (Department of Statistics, Kyungpook National University)
  • Received : 2015.03.24
  • Accepted : 2015.03.31
  • Published : 2015.04.30

Abstract

Elderly suicide rates tend to be high in Korea. Suicide by the elderly is no longer a personal problem; consequently, further research on risk and regional factors is necessary. Disease mapping in epidemiology estimates spatial patterns for disease risk over a geographical region. In this study, we use a simultaneous conditional autoregressive model for spatial correlations between neighboring areas to estimate standard mortality ratios and mapping. The method is illustrated with cause of death data from 2006 and 2010 to analyze regional patterns of elderly suicide in Korea. By considering spatial correlations, the Bayesian spatial models, mean educational attainment and percentage of the elderly who live alone was the significant regional characteristic for elderly suicide. Gibbs sampling and grid method are used for computation.

한국의 고령화는 매우 빠른 속도로 진행되고 있고, 노인자살은 노인의 주요 사망원인이며 노인은 다른 연력층보다 자살의 고위험군으로 알려져있다. 고령화 시대에서 노인의 자살은 사회적인 문제로 대두되고 있으며 이를 예방하기 위해 노인자살에 대한 위험요인을 파악하고, 지역적 차이를 확인하는 것이 중요하다. 특히 노인의 자살문제에서는 지역사회와의 통합결여 등이 큰 원인으로 고려되기 때문이다. 따라서, 본 논문에서는 공간적 상관관계를 고려하여 추정된 표준화사망률을 이용하여 질병지도를 작성하고자 하였다. 공간적 상관관계를 고려하기 위해서 simultaneous CAR model을 사용하였다. 2006년부터 2010년까지 통계청 사망자료를 이용하여 국내 시군구별 노인자살자수에 대해 두 모형을 적합시켜본 결과, 공간적 상관관계를 고려하지 않은 모형보다 공간적 상관관계를 고려한 모형이 더 좋은 모형임을 보였다. 또한 효율적인 베이지안 추론을 위해 격자망 방법 등을 고려하였다.

Keywords

References

  1. American Association of Suicidology (1999). Suicide Prevention Facilitator's Training Manual, Final report to the Department of the Navy, American Association of Suicidology, Washington, DC.
  2. Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems (with discussion), Journal of the Royal Statistical Society, Series B, 36, 192-236.
  3. Chang, S. S., Sterne, J. A., Wheeler, B. W., Lu, T. H., Lin, J. J. and Gunnell, D. (2011). Geography of suicide in Taiwan: Spatial patterning and socioeconomic correlates. Health Place, 17, 641-650. https://doi.org/10.1016/j.healthplace.2011.01.003
  4. Clayton, D. and Kaldor, J. (1987). Empirical Bayes estimates of age-standardizedr elative risks for use in disease mapping, Biometrics, 43, 671-681. https://doi.org/10.2307/2532003
  5. Gelfand, A. E., Dey, D. K. and Chang, H. (1992). Model determination using predictive distributions with implementation via sampling-based method (with discussion), In Bayesian Statistics 4, edited by J.M. Bernardo, et al., Oxford University Perss, Oxford, 147-167.
  6. Gelfand, A. E. and Ghosh, S. K. (1998). Model choice: A minimum posterior predictive loss approach, Biometrika, 85, 1-11. https://doi.org/10.1093/biomet/85.1.1
  7. Gunnell, D. (2005). Time trends and geographic differences in suicide: Implications for prevention. In: Hawton, K. (Ed.). Prevention and Treatment of Suicidal Behaviour: From Science to Practice, Oxford University Press, 293-306
  8. He, X. and Sun, D. (2000). Hierarchical Bay estimation of hunting success rates with spatial correlation, Biometrics, 56, 360-367. https://doi.org/10.1111/j.0006-341X.2000.00360.x
  9. Ji, J., Kleinman, A., and Becker, A. E. (2001). Suicide in contemporary China: A review of China's distinctive suicide demographics in their sociocultural context, Harvard Review of Psychiatry, 9, 1-12. https://doi.org/10.1080/10673220127875
  10. Levi, F., La Vecchia, C., Lucchini, F., Negri, E., Saxena, S., Maulik, P. K., and Saraceno, B. (2003). Trends in mortality from suicide, 1965-1999. Acta Psychiatrica Scandinavica, 108, 341-349. https://doi.org/10.1034/j.1600-0447.2003.00147.x
  11. Seo, D. W. (2003). Status of suicide rates and role of the Mental Health Center in Korea, 2nd Stakeholder's Workshop by 2003 Community Mental Health Services Project.
  12. Waller, A. L., Carlin, B. P., Xia, H. and Gelfand, A. E. (1997). Hierarchical Spatio-Temporal Mapping of Disease Rates, Journal of the American Statistical Association, 92, 607-617. https://doi.org/10.1080/01621459.1997.10474012