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

机译:在纵向出生队列研究中评估居民出行对空气污染暴露分类错误的影响

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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:为了在纵向队列研究中准确评估环境暴露,需要更好地了解住宅流动性记录不完整或不正确对暴露分类错误的影响。我们开发了用于生成地址暴露(ALGAE)的ALGorithm,可使用居住地址历史记录来分配空气污染暴露。我们将ALGAE应用于雅芳父母和儿童纵向研究(ALSPAC)的记录和历史每日空气污染评估(颗粒物质<10μm[PM10])。在1990年至1992年之间,ALSPAC在英格兰西南部招募了14,541名孕妇。对于每次怀孕,我们根据出生时居住地址处的PM10浓度,并使用重构的地址历史记录怀孕和生命的第一年中的活动性,对妊娠中期和生命中第一年的特定暴露估算值进行建模。我们能够将孕期特定的PM10暴露分配给12,752例妊娠,并将生命的第一年暴露分配给12,252例怀孕。怀孕期间的PM10平均暴露量为32.6μg/ m3(StDev 3.0μg/ m3),生命第一年的平均PM10暴露量为31.4μg/ m3(StDev 2.6μg/ m3)。 6%的妇女在怀孕期间搬家,而13%的妇女在出生后的第一年搬家。怀孕期间的暴露差异介于-5.3μg/ m3至12.4μg/ m3(高达26%)和生命第一年的-7.22μg/ m3至7.64μg/ m3(高达27%)之间,与使用完整清理的地址历史记录评估的暴露相比,出生时的地址。在出生队列研究的暴露评估中考虑居民流动性,可能会大大减少某些人的暴露分类错误。 ALGAE提供了一个开放源代码解决方案来清除存储在同类联系人数据库中的地址,并分配生命周期特定的风险。它的通用代码库意味着它适用于其他同类研究。

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