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Exposure Assessment in Cohort Studies of Childhood Asthma

机译:儿童哮喘队列研究中的暴露评估

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Background 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. Objective 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. Data synthesis 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. Conclusions 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.
机译:背景怀疑环境在儿童哮喘的发展中起重要作用。队列研究是用于研究暴露-反应关系的强大观察设计,但其功效部分取决于暴露评估的准确性。目的本文的目的是总结和讨论使准确的暴露评估成为挑战的问题,并提出改善儿童期哮喘和变态反应纵向队列研究中暴露评估的策略。数据综合需要对感兴趣的暴露进行优先排序,因为单个研究无法衡量所有可能相关的暴露。假设需要基于提出的机制,影响的关键时间窗,对物理,生理和免疫学发展的先验知识以及可能受到暴露影响的遗传途径。从公共卫生的角度来看,可修改的暴露量最为重要。鉴于有兴趣评估基因与环境之间的相互作用,因此需要大量的研究对象,因此规划独立研究中的数据池至关重要。可能通过受试者参与的方式收集其他样本将允许进行二次分析。结合了空气质量,环境和剂量数据的模型可提供大范围人群的接触估计,但仍可以改进。结论通过信息源的组合可以最好地描述暴露。改善暴露评估对于减少测量误差和提高功效至关重要,这可以增加对处于危险中的儿童进行表征的信心,从而改善健康状况。

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