Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
권호 표지
DOI QR코드

DOI QR Code

비만율 자료에 대한 베이지안 공간 분석

Bayesian spatial analysis of obesity proportion data

  • 최정순 (한양대학교 수학과, 한양대학교 자연과학연구소)
  • Choi, Jungsoon (Department of Mathematics, Hanyang University and Research Institute for Natural Sciences)
  • 투고 : 2016.08.06
  • 심사 : 2016.09.21
  • 발행 : 2016.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

비만은 그 자체가 질병이면서 다른 질병의 위험인자로 사회경제학적 요인과 관련성이 높다. 급증한 국내 비만인구에 대한 사회적 차원에서의 예방을 위하여 비만과 연관성이 있는 사회경제적 요인을 파악하는 것이 중요하다. 특히, 비만과 사회경제학적 요인간의 연관성은 성별에 따라 상이할 수 있으며 지역적 변동성 역시 존재한다. 본 논문에서는 공간적 상관성을 고려하여 비만율에 영향을 미치는 사회경제적 요인의 효과를 성별에 따라 추정하고자 한다. 공간적 상관성을 설명하기 위하여 베이지안 접근법을 기반으로 한 조건부 자기회귀모형을 고려하였다. 실증예제로 2010년 서울시 25개 자치구별 비만율 자료에 대하여 제안한 공간 모형과 공간적 상관성을 고려하지 않은 모형을 적합시켜본 결과, 공간적 상관성을 고려한 모형이 모형의 적합도와 예측력 측면에서 더 우수함을 알 수 있었다.

Obesity is a risk factor for various diseases as well as itself a disease and associated with socioeconomic factors. The obesity proportion has been increasing in Korea over about 15 years so that investigation of the socioeconomic factors related with obesity is important in terms of preventation of obesity. In particular, the association between obesity and socioeconomic status varies with gender and has spatial dependency. In the paper, we estimate the effects of socioeconomic factors on obesity proportion by gender, considering the spatial correlation. Here, a conditional autoregressive model under the Bayesian framework is used in order to take into account the spatial dependency. For the real applicaiton, we use the obestiy proportion dataset at 25 districts of Seoul in 2010. We compare the proposed spatial model with a non-spatial model in terms of the goodness-of-fit and prediction measures so the spatial model performs well.

키워드

참고문헌

  1. An, D., Han, J., Yoon, T., Kim, C. and Noh, M. (2015). Small area estimations for disease mapping by using spatial model. Journal of the Korean Data & Information Science Society, 26, 101-109. https://doi.org/10.7465/jkdi.2015.26.1.101
  2. Banerjee, S., Carlin, B. P. and Gelfand, A. E. (2004). Hierarchical modeling and analysis for spatial data, Chapman & Hall, New York.
  3. Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society B, 36, 192-236.
  4. Brook, D. (1964). On the distinction between the conditional probability and the joint probability approaches in the specification of nearest-neighbour systems. Biometrika, 51, 481-483. https://doi.org/10.1093/biomet/51.3-4.481
  5. Clifford, P. (1990). Markov random fields in statistics. In Disorder in Physical Systems. Oxford University Press, Oxford, 20-32.
  6. Cressie, N. (1993). Statistics for spatial data, John Wiley & Sons, New York.
  7. Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis, 1, 515-533. https://doi.org/10.1214/06-BA117A
  8. Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457-472. https://doi.org/10.1214/ss/1177011136
  9. Kang, H. T., Lee, H. R., Lee, Y. J., Linton, J. A. and Shim, J. Y. (2013). Relationship between employment status and obesity in a Korean elderly population, based on the 2007-2009 Korean National Health and Nutrition Examination Survey (KNHANES). Archives of gerontology and geriatrics, 57, 54-59. https://doi.org/10.1016/j.archger.2013.02.004
  10. Kang, J. H, Jeong, B. G., Cho, Y. G., Song, H. R. and Kim, K. A. (2011). Socioeconomic costs of overweight and obesity in Korean adults. Journal of Korean Medical Science, 26, 1533-1540. https://doi.org/10.3346/jkms.2011.26.12.1533
  11. Lee, W. J. and Park, C. (2015). Prediction of apartment prices per unit in Daegu-Gyeongbuk areas by spatial regression models. Journal of the Korean Data & Information Science Society, 26, 561-568. https://doi.org/10.7465/jkdi.2015.26.3.561
  12. McLaren, L. (2007). Socioeconomic status and obesity. Epidemiologic reviews, 29, 29-48. https://doi.org/10.1093/epirev/mxm001
  13. Ogden, C. L., Lamb, M. M., Carroll, M. D. and Flegal, K. M. (2010). Obesity and socioeconomic status in adults: United states, 2005-2008. NCHS data brief, 50, 1-8.
  14. Plummer, M., Best, N. G., Cowles, K. and Vines, K. (2006). coda: Convergence Diagnosis and Output Analysis for MCMC. R News, 6, 7-11.
  15. Sobal, J. and Stunkard, A. J. (1989). Socioeconomic status and obesity: A review of the literature. Psychological bulletin, 105, 260-275. https://doi.org/10.1037/0033-2909.105.2.260
  16. Spiegelhalter, D. J., Best, N., Carlin, B. P. and van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society B, 64, 583-639. https://doi.org/10.1111/1467-9868.00353
  17. Stern, H. S. and Cressie, N. (1999). Inference for extremes in disease mapping. In Disease Mapping and Risk Assessment for Public Health, edited by A. B. Lawson, A. Biggeri, D. Boehning, E. Lesaffre, J. F. Viel, and R. Bertolline, 63-84, Wiley, New York.
  18. World Health Organization. (2000). Obesity: Preventing and managing the global epidemic (No. 894), World Health Organization, Geneva.
  19. Yoo, S., Cho, H. J. and Khang, Y. H. (2010). General and abdominal obesity in South Korea, 1998-2007: Gender and socioeconomic differences. Preventive medicine, 51, 460-465. https://doi.org/10.1016/j.ypmed.2010.10.004