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为环境公共卫生追踪制定一致的数据和方法以衡量环境空气质量对公共卫生的影响:迄今进展与未来方向

Developing consistent data and methods to measure the public health impacts of ambient air quality for Environmental Public Health Tracking: progress to date and future directions.

作者信息

Talbot Thomas O, Haley Valerie B, Dimmick W Fred, Paulu Chris, Talbott Evelyn O, Rager Judy

出版信息

Air Qual Atmos Health. 2009 Dec;2(4):199-206. doi: 10.1007/s11869-009-0043-1. Epub 2009 Jul 14.

DOI:10.1007/s11869-009-0043-1
PMID:20098503
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2805787/
Abstract

Environmental Public Health Tracking (EPHT) staff at the state and national levels are developing nationally consistent data and methods to estimate the impact of ozone and fine particulate matter on hospitalizations for asthma and myocardial infarction. Pilot projects have demonstrated the feasibility of pooling state hospitalization data and linking these data to The United States Environmental Protection Agency (EPA) statistically based ambient air estimates for ozone and fine particulates. Tools were developed to perform case-crossover analyses to estimate concentration-response (C-R) functions. A weakness of analyzing one state at a time is that the effects are relatively small compared to their confidence intervals. The EPHT program will explore ways to statistically combine the results of peer-reviewed analyses from across the country to provide more robust C-R functions and health impact estimates at the local level. One challenge will be to routinely share data for these types of analyses at fine geographic and temporal scales without disclosing confidential information. Another challenge will be to develop C-R estimates which take into account time, space, or other relevant effect modifiers.

摘要

州和国家层面的环境公共卫生追踪(EPHT)工作人员正在开发全国统一的数据和方法,以评估臭氧和细颗粒物对哮喘和心肌梗死住院率的影响。试点项目已证明整合州住院数据并将这些数据与美国环境保护局(EPA)基于统计的臭氧和细颗粒物环境空气估计值相联系的可行性。开发了工具来进行病例交叉分析,以估计浓度-反应(C-R)函数。一次分析一个州的一个弱点是,与置信区间相比,其影响相对较小。EPHT计划将探索如何对来自全国各地经过同行评审的分析结果进行统计合并,以在地方层面提供更可靠的C-R函数和健康影响估计值。一个挑战将是在不泄露机密信息的情况下,定期在精细的地理和时间尺度上共享此类分析的数据。另一个挑战将是开发考虑时间、空间或其他相关效应修饰因素的C-R估计值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c130/2805787/b66129243e68/11869_2009_43_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c130/2805787/b66129243e68/11869_2009_43_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c130/2805787/b66129243e68/11869_2009_43_Fig1_HTML.jpg

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