基于空间模型的小概率地理健康事件生态学研究
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摘要
人类健康问题是当前国际地理学和环境科学领域的核心内容之一。本文选题基于地理信息系统和模型技术在小概率疾病的健康问题的空间分布规律和空间影响因子分析的应用研究。笔者不仅总结了传统的空间分析方法和模型,更针对小概率健康问题的空间分析中所遇到的问题提出相应的模型方法,并辅以实例来说明这些模型。本文主要从理论和实践两个层面展开研究。
(一)理论研究
本文回顾并总结了空间数据的分析方法和模型的基本理论,并在此基础上运用三种空间模型解决空间分析在环境健康领域应用中存在的若干问题。层次贝叶斯模型解决了因小概率健康事件引起的发病率或死亡率不稳定性(率的变化范围比较大)问题,将其调整到大体一致水平。风险探测器、危险因子探测器、生态探测器和交互作用探测器分别解决了风险因子在哪里、风险因子重要性、风险因子间的相对性及风险因素是独立起作用还是具有交互作用等问题。广义线性地统计模型将空间数据的空间关系代入到数据建模中,避免了可能出现的虚假统计意义。
(二)实际应用
本文使用层次贝叶斯模型对汶川地震五岁以下儿童死亡率的空间分布进行了调整并制作出死亡率的空间分布“热点”图。在此图的基础上,我们使用地理探测器对儿童死亡的环境因子进行了探索研究,结果表明地震烈度、房屋倒塌率和地形坡度对儿童死亡的影响较大,且属于正相关。这三个因子的两两间相互作用影响更大,而其他的环境因子如人口密度、高程、断裂带、地貌和GDP等对儿童死亡影响较小。
基于云南不明原因猝死的病例数据,进行模拟以探讨猝死与环境因子之间关系研究中,我们使用空间线性高斯模型(空间高斯线性模型)对猝死和环境因子间的关系进行拟合,并与高斯线性模型的拟合结果对比。结果表明NDVI(作为反映小白菌的生长环境)与猝死之间具有统计学意义,并排除了温度这一“虚假”影响因子。这也证实了小白菌与猝死之间的关系。但我们并没有发现降水等气候因子与猝死之间有显著的统计关系;此外,我们也没有发现地层和断裂带等地质因素对于猝死有显著的影响。
论文的最后,笔者根据研究过程中所遇到的问题,对整个研究作了总结并提出了今后研究的重点和方向。
Human health is currently a key part of study of geography and environmental science. The dissertation focuses on application of geographic information system and spatial model to spatial pattern and risk factors of geographic health. The author not only summarized the traditional methods of spatial analysis and model, but also tackled some problems in study of geographic health of small probability by proposing three spatial models which was then applied to two examples of geographic health issue. The dissertation was composed of the following two parts:
(1) Theoretical study
The dissertation firstly summarized methods of spatial data analysis and basic theory of spatial models, and then proposed three spatial models targeted at problems in application to geographical health of small probability. The Hierarchical Bayesian model was proposed to address the problem of instability of variance due to small cases of morbidity or mortality. Four geographical detectors were proposed to assess the environmental risks of health: the risk detector indicates where the risk areas are; the factor detector identifies factors that are responsible for the risk; the ecological detector discloses relative importance between the factors; and the interaction detector reveals whether the risk factors interact or lead to disease independently. And finally generalized linear geostatistical model was proposed to guard in assessment of environmental risks of health against spuriously significant covariate effects which might result from ignoring the spatial correlation inherent in the data.
(2) Practical application
The dissertation used the Hierarchical Bayesian model to map the spatial pattern of under-five mortality in Wenchuan at the township scale. Based on this map, we used geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) to assess effects of both physical factors and social-demographic factors on the under-five mortality. It was found that three factors are responsible for child mortality: earthquake intensity, housing collapse, and slope.
The generalized linear geostatistical model, or exactly speaking, spatial Gaussian linear model was employed to explore association between The Yunnan Sudden death Syndrome and environmental factors and compared to ordinary Gaussian linear model. It was found that only NDVI, as a proxy of the little white mushroom, had significant effect on sudden death although both NDVI and precipitation were indentified as significant factors by ordinary Gaussian linear model ignoring spatial correlation between death ratios. In addition, we did not find precipitation and geological factors, e.g. stratum and fault, had significant effect on sudden death.
In conclusion, the main content was summarized and the problems for further studied in this field were presented at the end of this dissertation.
(一)理论研究
本文回顾并总结了空间数据的分析方法和模型的基本理论,并在此基础上运用三种空间模型解决空间分析在环境健康领域应用中存在的若干问题。层次贝叶斯模型解决了因小概率健康事件引起的发病率或死亡率不稳定性(率的变化范围比较大)问题,将其调整到大体一致水平。风险探测器、危险因子探测器、生态探测器和交互作用探测器分别解决了风险因子在哪里、风险因子重要性、风险因子间的相对性及风险因素是独立起作用还是具有交互作用等问题。广义线性地统计模型将空间数据的空间关系代入到数据建模中,避免了可能出现的虚假统计意义。
(二)实际应用
本文使用层次贝叶斯模型对汶川地震五岁以下儿童死亡率的空间分布进行了调整并制作出死亡率的空间分布“热点”图。在此图的基础上,我们使用地理探测器对儿童死亡的环境因子进行了探索研究,结果表明地震烈度、房屋倒塌率和地形坡度对儿童死亡的影响较大,且属于正相关。这三个因子的两两间相互作用影响更大,而其他的环境因子如人口密度、高程、断裂带、地貌和GDP等对儿童死亡影响较小。
基于云南不明原因猝死的病例数据,进行模拟以探讨猝死与环境因子之间关系研究中,我们使用空间线性高斯模型(空间高斯线性模型)对猝死和环境因子间的关系进行拟合,并与高斯线性模型的拟合结果对比。结果表明NDVI(作为反映小白菌的生长环境)与猝死之间具有统计学意义,并排除了温度这一“虚假”影响因子。这也证实了小白菌与猝死之间的关系。但我们并没有发现降水等气候因子与猝死之间有显著的统计关系;此外,我们也没有发现地层和断裂带等地质因素对于猝死有显著的影响。
论文的最后,笔者根据研究过程中所遇到的问题,对整个研究作了总结并提出了今后研究的重点和方向。
Human health is currently a key part of study of geography and environmental science. The dissertation focuses on application of geographic information system and spatial model to spatial pattern and risk factors of geographic health. The author not only summarized the traditional methods of spatial analysis and model, but also tackled some problems in study of geographic health of small probability by proposing three spatial models which was then applied to two examples of geographic health issue. The dissertation was composed of the following two parts:
(1) Theoretical study
The dissertation firstly summarized methods of spatial data analysis and basic theory of spatial models, and then proposed three spatial models targeted at problems in application to geographical health of small probability. The Hierarchical Bayesian model was proposed to address the problem of instability of variance due to small cases of morbidity or mortality. Four geographical detectors were proposed to assess the environmental risks of health: the risk detector indicates where the risk areas are; the factor detector identifies factors that are responsible for the risk; the ecological detector discloses relative importance between the factors; and the interaction detector reveals whether the risk factors interact or lead to disease independently. And finally generalized linear geostatistical model was proposed to guard in assessment of environmental risks of health against spuriously significant covariate effects which might result from ignoring the spatial correlation inherent in the data.
(2) Practical application
The dissertation used the Hierarchical Bayesian model to map the spatial pattern of under-five mortality in Wenchuan at the township scale. Based on this map, we used geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) to assess effects of both physical factors and social-demographic factors on the under-five mortality. It was found that three factors are responsible for child mortality: earthquake intensity, housing collapse, and slope.
The generalized linear geostatistical model, or exactly speaking, spatial Gaussian linear model was employed to explore association between The Yunnan Sudden death Syndrome and environmental factors and compared to ordinary Gaussian linear model. It was found that only NDVI, as a proxy of the little white mushroom, had significant effect on sudden death although both NDVI and precipitation were indentified as significant factors by ordinary Gaussian linear model ignoring spatial correlation between death ratios. In addition, we did not find precipitation and geological factors, e.g. stratum and fault, had significant effect on sudden death.
In conclusion, the main content was summarized and the problems for further studied in this field were presented at the end of this dissertation.
引文
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Armenian, H. K. and A. Melkonian, et al. 1997. Deaths and injuries due to the earthquake in Armenia: A cohort approach. International Journal of Epidemiology 26: 806-813.
Bernardinelli, L. and C. Montomoli. 1992. Empirical Bayes versus fully Bayes analysis of geographical variation in disease risk. STATISTICS IN MEDICINE 11: 983-1007.
Berta, M. and G. A. Garlos, et al. 2008. Spatial analysis to identify hotspots of prevalence of schizophrenia. Social Psychiatry and Psychiatric Epidemiology 43 (10): 782-791.
Besag, J. A. N. J. 1991. The detection of clusters in rare diseases. Journal of the royal statistical society: 143-155.
Box, G. E. P. and D. R. Cox. 1964. An analysis of transformations (with discussion). Journal of the Royal Statistical Society, Series B 26: 211–252.
Breslow, N. E. and D. G. Clayton. 1993. Approximate inference in generalized linear mixed models. Journal of American Statistical Association 88: 9-25.
Chen, C. T. and T. T. Wang. 2009. New slope indexes for landslide potential analysis in debris flow Stream. Geophysical Research Abstracts 11: 5615.
Chiles, J. P. and P. Del?ner (1999). Geostatistics: Modeling Spatial Uncertainty. New York, Wiley.
Clark, J. S. (2007). Models for Ecological Data: An Introduction, Princeton University Press.
Clayton, D. and J. Kaldor. 1987. Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics 43: 671-681.
Cox, L. H. 1996. Protecting confidentiality in small population health and environmental statistics. Statistics in Medicine 15: 1895-1905.
Cressie, N. (1993). Statistics for spatial data. New York, Wiley.
Diggle, P. J. and Jr. P. J. Ribeiro (2007). Model-based Geostatistics. Berlin, Springer.
Fisher, N. I. T. L. and B. J. J. Embleton (1987). Statistical Analysis Of Spherical Data, Cambridge University Press: 329.
Gelman, A. and P. N. Price. 1999. All maps of parameter estimates are misleading. Stat Med 18 (23): 3221-34.
Gutierrez, E. and F. Taucer, et al. 2005. Analysis of worldwide earthquake mortality using multivariate demographic and seismic data. American Journal of Epidemiology 161: 1151-1158. Haining, R. (1984). Spatial Data Analysis in the Social and Environmental Science. London, Cambridge University Press.
Haining, R. (2003). Spatial Data Analysis: Theory and Practice. Cambridge, Cambridge university press.
Horwich, G. H. 2000. Economic lessons of the Kobe earthquake. ECONOMIC DEVELOPMENT AND CULTURAL CHANGE 48: 521-542.
Jordan, M. I. and Z. Ghahramani (1999). An introduction to variational methods for graphical models. Learning in graphical models. Cambridge, MIT Press.
Journel, A. G. and C. J. Huijbregts (1978). Mining Geostatistics. London, Academic Press.
Kahn, M. K. 2005. The death toll from natural disasters: The role of income, geography, and institutions. Review of Economics and Statistics 87: 271-284.
Kousa, A. and E. Moltchanova, et al. 2004. Geochemistry of ground water and theincidence of acute myocardial infarction in Finland. J Epidemiol Community Health 58 (2): 136-9.
Kulldorff, M. 1997. A spatial scan statistic. Communications in Statistics: Theory and Methods 26: 1481-96.
Liang, K. Y. and S. L. Zeger. 1986. Longitudinal data analysis using generalized linear models. Biometrika 73: 13-22.
Lieping, Y. and L. Xinzheng, et al. 2008. Analysis on Building Seismic Damage in the Wenchuan Earthquake. The 14th World Conference on Earthquake Engineering. Beijing, China.
Lin, N. F. and J. Tang, et al. 2004. Geochemical environment and health problems in China. Environ Geochem Health 26 (1): 81-8.
Lindeman, R. D. and J. R. Assenzo. 1964. Correlations between water hardness and cardiovascular deaths in Oklahoma counties. Am J Public Health Nations Health 54: 1071-7.
Liu, Z. and S. Sun. 2009. The Disaster of May 12th Wenchuan Earthquake and Its Influence on Debris Flows. Journal of Geography and Geology 1 (1): 26-30.
Loukola-Ruskeeniemi, K. and M. Kantola, et al. 1999. Migration of potentially harmful elements from black shales to aquatic ecosystems and local residents preliminary result in eastern Finland. Geological Survey of Finland: 107-110.
Mardia, K. V. and A. J. Watkins. 1989. On multimodality of the likelihood in the spatial linear model. Biometrika 76: 289-296.
Marmota, M. G. 2001. Alcohol and coronary heart disease. International Journal of Epidemiology 30 (4): 724-729.
Matérn, B. (1986). Spatial Variation (second edn). Berlin, Springer. McCullagh, P. and J. A. Nelder (1989). Generalized Linear Models, second edn. London, Chapman and Hall.
Nelder, J. A. and R. M. Wedderburn. 1972. Generalized linear models. Journal of the Royal Statistical Society Series A-Statistics In Society 135: 370-84.
NG, A. Y. and M. I. Jordan (2000). Approximate inference algorithms for two-layer Bayesian networks. Advances in neural information processing systems.Cambridge, MIT Press.
O'Hagan, A. (1994). Bayesian Inference. Vol.2bof Kendall's Advanced Theory of Statistics, Edward Arnold.
Osaki, Y. and M. Minowa. 1999. Factors associated with earthquake deaths in the Great Hanshin-Awaji Earthquake, 1995. Nippon Koshu Eisei Zasshi 46 (3): 175-83.
Pai, C. and Y. Tien, et al. 2007. A study of the human-fatality rate in near-fault regions using the Victim Attribute Database. Natural Hazards 42: 19-35.
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