Assessing the association between environmental impacts and health outcomes: a case study from Florida.
Assessing the association between environmental impacts and health outcomes: a case study from Florida.
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评估环境影响与健康结果之间的关联：佛罗里达州的案例研究。
DOI：
10.1002/sim.3249
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发表时间：
2008
影响因子：
2
通讯作者：
DuClos,Chris
中科院分区：
文献类型：
--
作者：
Young,LindaJ;Gotway,CarolA;Yang,Jie;Kearney,Greg;DuClos,Chris
疾病控制和预防中心（CDC）创建了环境公共卫生跟踪（EPHT）计划，将危害监测，暴露和健康影响监测整合到一个有凝聚力的跟踪网络中。部分佛罗里达的努力走向实施EPHT是开发心肌梗死（MI）和佛罗里达的环境臭氧水平之间的空间和时间关联的模型。现有数据来自佛罗里达卫生保健管理局、佛罗里达环境保护部、美国人口普查局和疾病预防控制中心的行为风险因素监测系统。这些数据通过忽略空间支持和使用块克里金法（一种支持调整方法）进行关联。MI数据通过年龄、人种/种族和性别间接标准化。以佛罗里达州作为比较标准，计算每个县和每个月的MI标准化事件比率（SER）。在数据连接之后，最初使用全球模型将MI与环境臭氧水平联系起来，并对协变量进行调整。全局模型提供状态的估计相对MI SER。认识到MI和臭氧的关联可能会在不同地点发生变化，当地模型被用来估计每个县的相对MI SER，再次调整协变量。结果不同，这取决于是否空间支持被忽略或占模型。与EPHT分析的机遇和挑战进行了讨论，并强调了未来的发展方向。版权所有© 2008约翰威利父子有限公司。
The Centers for Disease Control and Prevention (CDC) created the Environmental Public Health Tracking (EPHT) program to integrate hazard monitoring, exposure, and health effects surveillance into a cohesive tracking network. Part of Florida's effort to move toward implementation of EPHT is to develop models of the spatial and temporal association between myocardial infarctions (MIs) and ambient ozone levels in Florida. Existing data were obtained from Florida's Agency for Health Care Administration, Florida's Department of Environmental Protection, the U.S. Census Bureau, and CDC's Behavioral Risk Factor Surveillance System. These data were linked by both ignoring spatial support and using block kriging, a support‐adjusted approach. The MI data were indirectly standardized by age, race/ethnicity, and sex. The state of Florida was used as the comparison standard to compute the MI standardized event ratio (SER) for each county and each month. After the data were linked, global models were used initially to relate MIs to ambient ozone levels, adjusting for covariates. The global models provide an estimated relative MI SER for the state. Realizing that the association in MIs and ozone might change across locations, local models were used to estimate the relative MI SER for each county, again adjusting for covariates. Results differed, depending on whether the spatial support was ignored or accounted for in the models. The opportunities and challenges associated with EPHT analyses are discussed and future directions highlighted. Copyright © 2008 John Wiley & Sons, Ltd.