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Assessing the Effects of Residential Mobility on Air Pollution Exposure Misclassification in Longitudinal Birth Cohort Studies

机译:评估住宅流动性对纵向出生队列研究中空气污染暴露错误分类的影响

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To accurately assess environmental exposures in longitudinal cohort studies, a better understanding is needed about the effects of incomplete or inaccurate records of residential mobility on exposure misclassification. We developed an ALGorithm for Generating Address Exposures (ALGAE) that assigns air pollution exposure using residential address histories. We applied ALGAE to address records and historic daily air pollution estimates (particulate matter <10 μm [PM10]) for the Avon Longitudinal Study of Parents and Children (ALSPAC). ALSPAC recruited 14,541 pregnant women between 1990 and 1992 in the South West of England. For each pregnancy, we modelled pregnancy trimester- and first year of life-specific exposure estimates based on PM10 concentrations at residential address at birth and using reconstructed address histories to account for mobility during pregnancy and first year of life. We were able to assign trimester-specific PM10 exposures for 12,752 pregnancies and first year of life exposures for 12,252 pregnancies. Average PM10 exposures were 32.6 μg/m3 (StDev 3.0 μg/m3) during pregnancy and 31.4 μg/m3 (StDev 2.6 μg/m3) during first year of life. 6% of women moved address during pregnancy, and 13% moved during first year of life. Exposure differences ranged from -5.3 μg/m3 to 12.4 μg/m3 (up to 26%) during pregnancy and -7.22 μg/m3 to 7.64 μg/m3 (up to 27%) in first year of life between the exposure assessed using the address at birth compared to the exposure assessed using the complete cleaned address history. Accounting for residential mobility in the exposure assessment of birth cohort studies has the potential to substantially reduce exposure misclassification for some individuals. ALGAE provides an open-source solution to clean addresses stored in the cohort contact database and assigns life-stage specific exposures. Its generic code base means that it is adaptable for other cohort studies.
机译:为了准确评估纵向队列研究环境暴露,需要有关的住宅流动性的不完整或不准确的记录暴露分类错误的影响的了解。我们开发了一种算法,用于生成地址曝光(藻类)使用住址历史指派空气污染暴露。我们应用藻类地址记录和历史的每日空气污染估计(颗粒物<10微米[PM10])父母和孩子的雅芳纵向研究(ALSPAC)进行。阿尔斯普克在1990年至1992年间在英格兰西南部招募了14,541名孕妇。对于每次怀孕,我们模拟怀孕trimester-和出生时和怀孕和生活的第一年期间使用重建的地址历史记录,以考虑流动性基础上的住址PM10浓度具体的生活曝光估计第一年。我们能够孕期特有的分配PM10暴露于12752名怀孕和生活中的接触为12,252怀孕第一年。平均PM10暴露在怀孕期间是32.6微克/立方米(3.0 STDEV微克/立方米)和31.4微克/立方米在生命的第一年(2.6 STDEV微克/立方米)。女性的6%,在怀孕期间移动的地址,在生命的第一年,13%的移动。在生命的第一年期间怀孕和-7.22微克/立方米曝光差异的范围为-5.3微克/立方米至12.4微克/立方米(高达26%),以7.64微克/立方米(高达27%)之间使用的曝光评估出生地址相比,使用完整的清洗地址历史评估曝光。占出生队列研究的暴露评估住宅流动性有可能大大减少误判暴露一些个人的潜力。藻类为清洁存储在队列联系人数据库中的清洁地址提供了一个开源解决方案,并为生命阶段特定的曝光分配。其通用代码基因意味着它适用于其他队列研究。

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