Vanoli Jacopo, Mistry Malcolm N, De La Cruz Libardi Arturo, Masselot Pierre, Schneider Rochelle, Ng Chris Fook Sheng, Madaniyazi Lina, Gasparrini Antonio
School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, UK.
J Expo Sci Environ Epidemiol. 2024 Nov;34(6):1012-1017. doi: 10.1038/s41370-023-00635-w. Epub 2024 Jan 8.
Recent developments in linkage procedures and exposure modelling offer great prospects for cohort analyses on the health risks of environmental factors. However, assigning individual-level exposures to large population-based cohorts poses methodological and practical problems. In this contribution, we illustrate a linkage framework to reconstruct environmental exposures for individual-level epidemiological analyses, discussing methodological and practical issues such as residential mobility and privacy concerns. The framework outlined here requires the availability of individual residential histories with related time periods, as well as high-resolution spatio-temporal maps of environmental exposures. The linkage process is carried out in three steps: (1) spatial alignment of the exposure maps and residential locations to extract address-specific exposure series; (2) reconstruction of individual-level exposure histories accounting for residential changes during the follow-up; (3) flexible definition of exposure summaries consistent with alternative research questions and epidemiological designs. The procedure is exemplified by the linkage and processing of daily averages of air pollution for the UK Biobank cohort using gridded spatio-temporal maps across Great Britain. This results in the extraction of exposure summaries suitable for epidemiological analyses of both short and long-term risk associations and, in general, for the investigation of temporal dependencies. The linkage framework presented here is generally applicable to multiple environmental stressors and can be extended beyond the reconstruction of residential exposures. IMPACT: This contribution describes a linkage framework to assign individual-level environmental exposures to population-based cohorts using high-resolution spatio-temporal exposure. The framework can be used to address current limitations of exposure assessment for the analysis of health risks associated with environmental stressors. The linkage of detailed exposure information at the individual level offers the opportunity to define flexible exposure summaries tailored to specific study designs and research questions. The application of the framework is exemplified by the linkage of fine particulate matter (PM) exposures to the UK Biobank cohort.
连锁程序和暴露建模的最新进展为环境因素健康风险的队列分析提供了广阔前景。然而，将个体层面的暴露情况应用于大型基于人群的队列研究存在方法学和实际操作方面的问题。在本文中，我们阐述了一个连锁框架，用于重建个体层面流行病学分析的环境暴露情况，并讨论了诸如居住流动性和隐私问题等方法学和实际问题。这里概述的框架需要有相关时间段内的个体居住历史，以及高分辨率的环境暴露时空地图。连锁过程分三步进行：（1）将暴露地图和居住地点进行空间对齐，以提取特定地址的暴露序列；（2）考虑随访期间居住变化，重建个体层面的暴露历史；（3）根据不同的研究问题和流行病学设计，灵活定义暴露总结。该程序以英国生物银行队列的空气污染日均值连锁和处理为例进行说明，使用了大不列颠地区的网格化时空地图。这使得能够提取适合短期和长期风险关联流行病学分析的暴露总结，一般而言，适合于研究时间依赖性。这里提出的连锁框架通常适用于多种环境压力源，并且可以扩展到居住暴露重建之外。影响：本文描述了一个连锁框架，用于利用高分辨率时空暴露将个体层面的环境暴露应用于基于人群的队列研究。该框架可用于解决当前环境压力源健康风险分析中暴露评估的局限性。个体层面详细暴露信息的连锁提供了机会，以定义适合特定研究设计和研究问题的灵活暴露总结。该框架的应用以细颗粒物（PM）暴露与英国生物银行队列的连锁为例进行说明。
