Journal of the Korean Geographical Society (대한지리학회지)

The Korean Geographical Society (대한지리학회)
권호 표지

Mapping the Geographic Variations of the Low Birth Weight cases in South Korea: Bayesian Approaches

우리나라 저체중아 출생의 공간적 변동성 지도화: 베이지언적 접근

  • Roh, Young-hee (Division of Natural Resources Conservation, Korea Environment Institute) ;
  • Park, Key-ho (Department of Geography, Seoul National University)
  • Received : 2016.04.06
  • Published : 2016.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study reviewed and compared methods for mapping aggregated low birth weight (LBW) and geographic variations in LBW in South Korea. Based on this review, we produced LBW maps in South Korea. Standardized mortality/morbidity ratios (SMRs) and crude mortality rates have been widely used for many years in epidemiological research. However, SMR-based maps are likely to be affected by sample size of unit area. Therefore, this study adopted a model-based approach using Bayesian estimates to reduce noisy variability in the SMR. By using a Bayesian model, we can calculate a statistically reliable RR values. We used the full Bayes estimator, as well as empirical Bayes estimators. As a result, variations in the two Bayes models were similar. The SMR-based statistics had the largest variation. The result maps can be used to identify regions with a high risk of LBW in South Korea.

본 연구에서는 우리나라에서 발생한 저체중아 출생 집계 자료를 공간적으로 지도화하기 위한 기법들을 검토 비교하고, 이를 기반으로 우리나라의 LBW 지도를 작성하였다. 표준화사망률이나 조사망률 등은 역학 분야에서 지속적으로 광범위하게 사용되고 있는 지표이다. 그러나 이러한 표준화사망률은 집계 단위의 샘플 수에 영향을 많이 받는다는 단점을 가지고 있다. 이에, 본 연구에서는 베이지언 기법을 활용하여 샘플 수에 따른 통계적 변동성을 감소시키고자 하였다. 이를 위해 경험적 베이지언 기법과 풀 베이지언 기법을 모두 활용하였고, 결과적으로 유사한 통계량을 산출한 것을 확인할 수 있었다. 반면, SMR 기반의 통계량은 높은 분산을 가지고 있음을 확인하였다. 연구의 결과에 따른 통계 지도는 우리나라 저체중아 출생의 높은 위험도를 가지는 지역들을 파악할 수 있도록 한다.

Keywords

References

  1. 통계청, 2015, 2015 통계로 보는 여성의 삶.
  2. Berkowitz, G.S., Skovron, M.L., Lapinski, P.H. and Berkowitz, R.L., 1990, Delayed childbearing and the outcome of pregnancy, The New England Journal of Medicine, 322, 659-664. https://doi.org/10.1056/NEJM199003083221004
  3. Bernardinelli, L. and Montomoli, C., 1992, Empirical Bayes versus fully Bayesian analysis of geographical variation in disease risk, Statistics in Medicine, 11, 983-1007. https://doi.org/10.1002/sim.4780110802
  4. Besag, J., York, J., and Mollie, A., 1991, Bayesian Image Restoration, with Two Applications in Spatial Statistics, Annals of the Institute of Statistical Mathematics, 43, 1-59. https://doi.org/10.1007/BF00116466
  5. Bureau of Health Policy, Ministry of Health & Welfare, 2011, 2012 Family health service guide.
  6. Carriquiry, L. and Pawlovich, P., 2004, From Empirical Bayes to Full Bayes: Methods for Analyzing Traffic Safety Data, Iowa Library Services, URI: http://publications.iowa.gov/id/eprint/13273.
  7. Chomitz, V.R., Cheung, L.W.Y. and Lieberman, E., 1995, The role of lifestyle in preventing low birth weight, The Future of Children, 5(1), 121-138. https://doi.org/10.2307/1602511
  8. Clayton, D.G. and Kaldor, J., 1987, Empirical Bayes estimates of age-standardized relative risks for use in disease mapping, Biometrics, 43(3), 671-681. https://doi.org/10.2307/2532003
  9. Clayton, D.G.,1989, A Monte Carlo Method for Bayesian Inference in Frailty Models, University of Leicester Department of Community Health Technical Report, Leicester, U.K.
  10. Costello, J., Ortmeyer, C.E. and Morgan, W.K.C., 1974, Mortality from lung cancer in U.S. coal miners, American Journal of Public Health, 64(3), 222-224. https://doi.org/10.2105/AJPH.64.3.222
  11. Elsen, E.A., Tolbert, P.E., Monson, R.R. and Smith, T.J., 1992, Mortality studies of machining fluid exposure in the automobile industry I: A standardized mortality ratio analysis, American Journal of Industrial Medicine, 22, 809-824. https://doi.org/10.1002/ajim.4700220604
  12. Geronimus, A.T., 1996, Black/white differences in the relationship of maternal age to birthweight: A population-based test of the weathering hypothesis, Social Science & Medicine, 42(4), 589-597. https://doi.org/10.1016/0277-9536(95)00159-X
  13. Kennedy, 1989, The small number problem and the accuracy of spatial databases, in Goodchild, M.F. and Gopal, S. eds., Accuracy of Spatial Databases, Taylor & Francis, U.K., London.
  14. Lawson, A.B., 2006, Statistical Methods in Spatial Epidemiology, 2nd ed., John Wiley & Sons Inc, UK.
  15. Lawson, A.B., Biggeri, A.B., Bohning, D., Lesaffre, E., Viel, J.F., Clark, A., Schlattmann, P. and Divino, F., 2000, Disease mapping models: an empirical evaluation, Statistics in Medicine, 19, 2217-2241. https://doi.org/10.1002/1097-0258(20000915/30)19:17/18<2217::AID-SIM565>3.0.CO;2-E
  16. Lawson, A.B., Bohning, D., Biggeri, A., Lesaffre, E. and Viel, J.F., 1999, Disease mapping and its uses, in Lawson, A.B., Bohning, D., Biggeri, A., Lesaffre, E. and Viel, J.F. and Bertollini, R. eds., Disease mapping and risk assessment for public health, West Sussex, John Wiley & Sons, U.K.
  17. Lewis, G.H. and Johnson, R.G., 1971, Kendall's Coefficient of Concordance for sociometric rankings with self-excluded, Sociometry, 34, 469-503.
  18. Marshall, R.J., 1991, Mapping disease and mortality rates using empirical Bayes estimators, Applied Statistics, 40(2), 283-294. https://doi.org/10.2307/2347593
  19. Meliker, J.R., Wahl, R.L., Cameron, L.L. and Nriagu, J.O., 2007, Arsenic in drinking water and cerebrovascular disease, diabetes mellitus, and kidney disease in Michigan: a standardized mortality ratio analysis, Environmental Health, 6(4), 1-11. https://doi.org/10.1186/1476-069X-6-1
  20. Mollie, A. and Richardson, S., 1991, Empirical Bayes estimates of cancer mortality rates using spatial models, Statistical in Medicine, 10, 95-112. https://doi.org/10.1002/sim.4780100114
  21. Mollie, A., 1999, Bayesian and empirical Bayes approaches to disease mapping, In Disease Mapping and Risk Assessment for Public Health, Lawson, A.B., Biggeri, A., Boehning D, Lesaffre E, Viel, J-F., Bertollini, R. (eds), Wiley, New York, 15-29.
  22. Persaud, B., Lan, B., Lyon, C. and Bhim, R., 2010, Comparison of empirical Bayes and full Bayes approaches for before-after road safety evaluations, Accident Analysis and Prevention, 42, 38-43. https://doi.org/10.1016/j.aap.2009.06.028
  23. Pickle, L.W. and White, A.A., 1995, Effects of the choice of age-adjustment method on maps of death rates, Statistics in Medicine, 14, 615-627. https://doi.org/10.1002/sim.4780140519
  24. Richardson, S., Thomson, A., Best, N. and Elliott, P., 2004, Interpreting posterior relative risk estimates in disease-mapping studies, Environmental Health Perspectives, 112(9), 1016-1025. https://doi.org/10.1289/ehp.6740
  25. Rush, D. and Cassano, P., 1983, Relationship of cigarette smoking and social class to birth weight and perinatal mortality among all births in Britain, 5-11 April 1970, Journal of Epidemiology and Community Health, 37, 249-255. https://doi.org/10.1136/jech.37.4.249
  26. Shiono, P.H. and Behrman, R.E., 1995, Low birth weight: analysis and recommendations, Future Child, 5(1), 4-18. https://doi.org/10.2307/1602504
  27. Song, S.H. and Choi, E.S., 1999, Clinical Observation on Delivery of Low Birth Weight Infant, Journal of Korean Academy of Women's Health Nursing, 5(2), pp.169-178.
  28. Symons, M.J. and Taulbee, J.D., 1980, Standardized mortality ratio as approximation to relative risk, Institute of Statistics Mimeo Series No.1294.
  29. Tobler, W., 1970, A computer movie simulating urban growth in the Detroit region, Economic Geography, 46(2), 234-240. https://doi.org/10.2307/143141
  30. Valero De Bernabe, J., Soriano, T., Albaladejo, R., Juarranz, M., Calle, M.E., Martinez, D. and Dominguez-Rojas, V., 2004, Risk factors for low birth weight: a review, European Journal of Obstetrics & Gynecology and Reproductive Biology, 116, 3-15. https://doi.org/10.1016/j.ejogrb.2004.03.007
  31. World Health Organization, 1950, Expert Group on Prematurity. Final report. Technical report series 27, World Health Organization, Geneva.
  32. Ylppo A., 1919, Zur physiologie, klinik, zum schicksal der fruhgeborenen, Zeitschrift fur kinderheilkunde, 24, 1-110. https://doi.org/10.1007/BF02222072
  33. Young-hee Roh and Key-ho Park, 2014, A Comparative Analysis of SMR vs. Bayesian Modeling for Calculating Statistically Reliable Relative Risks and Disease Mapping, 한국지도학회지, 14(2), 107-117.