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The application of spatial statistics to environmental epidemiology.

机译:空间统计在环境流行病学中的应用。

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摘要

This body of work applies an interdisciplinary approach to methodological issues in environmental epidemiology research. Exposure assessment, precision, confounding and scale are addressed by integrating knowledge from the fields of environmental health sciences, epidemiology and spatial statistics. Environmental health sciences provide myriad tools for the improvement of exposure assessment in environmental epidemiology. Similar contributions from the rapidly growing field of spatial statistics have not yet been fully explored, particularly with regard to bridging environmental health science exposure measurement and epidemiology research design. Using studies of air pollution and asthma as an example of the wider field of environmental epidemiology, this work reviews recent approaches to exposure assessment and areas in which spatial statistics can provide methodological improvements. Precision and exposure assessment are addressed through a power evaluation of hypothesis tests and models to detect focused clustering, disease clustering associated with a known pollution source. Environmental health science tools describe the spatial dispersion of environmental contaminants. Human exposure, and thus disease, should follow these spatial dispersion patterns. The power evaluations in this work investigate the ability of focused cluster hypothesis tests and models to detect disease cluster shapes that are based on pollution dispersion principles. Findings show the importance of mathematical functions that reflect the particular pollution dispersion factors in a given cluster investigation. Finally, a study of the association of asthma with ozone, PM10 and PM2.5 is conducted as an applied example of methodologically innovative interdisciplinary research. Bayesian kriging and spatial prediction of environmental variables are used in this investigation. Data are analyzed with generalized linear mixed models that include random effects addressing individual frailty and geographic area. Not only is the feasibility of these techniques demonstrated but significant associations are evident between PM 10 and asthma ED admissions among children in South Carolina. In sum, this body of work provides evidence that the crossing of traditional disciplinary boundaries can advance methodological issues in environmental epidemiology research. Challenges include the time and effort involved in conducting studies that are interdisciplinary from design to completion. Yet, these collaborations can prove as rewarding to the researchers as to the quality of the research.
机译:这项工作将跨学科方法应用于环境流行病学研究中的方法论问题。通过整合来自环境健康科学,流行病学和空间统计领域的知识,可以解决暴露评估,精确度,混淆和规模问题。环境卫生科学为改善环境流行病学中的接触评估提供了多种工具。尚未充分探索迅速发展的空间统计领域的类似贡献,特别是在桥接环境健康科学暴露测量和流行病学研究设计方面。以空气污染和哮喘的研究为例,它作为更广泛的环境流行病学领域的一个例子,这项工作回顾了最近的暴露评估方法以及空间统计可以在方法上进行改进的领域。通过对假设检验和模型的功效评估来解决精度和暴露评估问题,以检测集中聚类,与已知污染源相关的疾病聚类。环境健康科学工具描述了环境污染物的空间扩散。人体暴露,进而疾病,应遵循这些空间分散模式。这项工作中的功率评估研究了基于污染扩散原理的集中聚类假设检验和模型检测疾病聚类形状的能力。结果表明,在给定的分类研究中,反映特定污染扩散因子的数学函数的重要性。最后,对哮喘与臭氧,PM10和PM2.5的关系进行了研究,以此作为方法创新的跨学科研究的应用实例。本研究使用贝叶斯克里金法和环境变量的空间预测。使用广义线性混合模型分析数据,该模型包括针对个体脆弱性和地理区域的随机效应。不仅证明了这些技术的可行性,而且在南卡罗来纳州儿童中,PM 10与哮喘ED入院之间也存在明显的关联。总而言之,这项工作提供了证据,证明跨越传统学科界限可以推进环境流行病学研究中的方法论问题。挑战包括进行从设计到完成的跨学科研究所需的时间和精力。然而,这些合作可以证明对研究人员的回报是研究质量。

著录项

  • 作者

    Puett, Robin Caroline.;

  • 作者单位

    University of South Carolina.;

  • 授予单位 University of South Carolina.;
  • 学科 Health Sciences Public Health.; Biology Biostatistics.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 172 p.
  • 总页数 172
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 预防医学、卫生学;生物数学方法;
  • 关键词

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