Dalla Lana School of Public Health, Gage Occupational and Environmental Health Unit, University of Toronto, Toronto, Ontario, Canada.
Environ Health Perspect. 2011 May;119(5):591-7. doi: 10.1289/ehp.1002267. Epub 2010 Nov 16.
The environment is suspected to play an important role in the development of childhood asthma. Cohort studies are a powerful observational design for studying exposure-response relationships, but their power depends in part upon the accuracy of the exposure assessment.
The purpose of this paper is to summarize and discuss issues that make accurate exposure assessment a challenge and to suggest strategies for improving exposure assessment in longitudinal cohort studies of childhood asthma and allergies.
Exposures of interest need to be prioritized, because a single study cannot measure all potentially relevant exposures. Hypotheses need to be based on proposed mechanisms, critical time windows for effects, prior knowledge of physical, physiologic, and immunologic development, as well as genetic pathways potentially influenced by the exposures. Modifiable exposures are most important from the public health perspective. Given the interest in evaluating gene-environment interactions, large cohort sizes are required, and planning for data pooling across independent studies is critical. Collection of additional samples, possibly through subject participation, will permit secondary analyses. Models combining air quality, environmental, and dose data provide exposure estimates across large cohorts but can still be improved.
Exposure is best characterized through a combination of information sources. Improving exposure assessment is critical for reducing measurement error and increasing power, which increase confidence in characterization of children at risk, leading to improved health outcomes.
环境被怀疑在儿童哮喘的发展中起着重要作用。队列研究是研究暴露-反应关系的有力观察设计,但它们的功效部分取决于暴露评估的准确性。
本文旨在总结和讨论使准确暴露评估成为挑战的问题,并提出改善儿童哮喘和过敏纵向队列研究中暴露评估的策略。
需要优先考虑感兴趣的暴露,因为单个研究不可能测量所有潜在相关的暴露。假设需要基于提出的机制、影响的关键时间窗口、物理、生理和免疫发育的先验知识以及潜在受暴露影响的遗传途径。从公共卫生的角度来看,可改变的暴露是最重要的。鉴于评估基因-环境相互作用的兴趣,需要大型队列,并且必须规划跨独立研究的数据汇集。通过研究对象的参与收集额外的样本可能会进行二次分析。结合空气质量、环境和剂量数据的模型为大型队列提供暴露估计值,但仍可以改进。
通过多种信息来源来最好地描述暴露情况。改善暴露评估对于减少测量误差和提高功效至关重要,这可以提高对风险儿童的特征描述的信心,从而改善健康结果。