A flexible hierarchical framework for improving inference in area-referenced environmental health studies.
A flexible hierarchical framework for improving inference in area-referenced environmental health studies.
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DOI：
10.1002/bimj.201900241
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发表时间：
2020-11
期刊：
影响因子：
--
通讯作者：
Blangiardo M
中科院分区：
文献类型：
--
作者：
Pirani M;Mason AJ;Hansell AL;Richardson S;Blangiardo M
按地理区域汇总数据的研究设计在环境流行病学中很受欢迎。这些研究通常基于行政数据库，提供了完整的空间覆盖范围，特别吸引人对整个人口进行推断。然而，由于未测量的混杂因素，由此产生的估计往往是有偏见的，很难解释，而这些混杂因素通常不能从常规收集的数据中获得。我们提出了一个框架，以改进从这类研究中得出的推论，这些研究利用了来自个人层面调查数据的信息。后者通过在生态层面上模仿众所周知的倾向评分方法，在区域一级的标量评分中加以总结。关于混杂调整倾向分数的文献主要是基于个体水平的研究，并假设一个二元暴露变量。在这里，我们概括地使用它来处理以连续暴露为特征的区域参考研究。我们的方法基于两阶段设计的贝叶斯层次结构：(I)调查样本的地理位置个体水平数据在生态水平上被放大，后者被用来估计样本内区域的广义生态倾向得分(EPS)；(Ii)在不同的关于缺失机制的假设下，广义生态倾向得分被赋予样本外区域，然后将其纳入生态回归，将兴趣暴露与健康结果联系起来。这提供了区域级别的风险估计，允许比传统的区域研究更全面地调整混乱。该方法是通过使用模拟和一项调查英国(英国)与二氧化氮相关的肺癌死亡风险的案例研究来说明的。
Study designs where data have been aggregated by geographical areas are popular in environmental epidemiology. These studies are commonly based on administrative databases and, providing a complete spatial coverage, are particularly appealing to make inference on the entire population. However, the resulting estimates are often biased and difficult to interpret due to unmeasured confounders, which typically are not available from routinely collected data. We propose a framework to improve inference drawn from such studies exploiting information derived from individual-level survey data. The latter are summarized in an area-level scalar score by mimicking at ecological level the well-known propensity score methodology. The literature on propensity score for confounding adjustment is mainly based on individual-level studies and assumes a binary exposure variable. Here, we generalize its use to cope with area-referenced studies characterized by a continuous exposure. Our approach is based upon Bayesian hierarchical structures specified into a two-stage design: (i) geolocated individual-level data from survey samples are up-scaled at ecological level, then the latter are used to estimate a generalized ecological propensity score (EPS) in the in-sample areas; (ii) the generalized EPS is imputed in the out-ofsample areas under different assumptions about the missingness mechanisms, then it is included into the ecological regression, linking the exposure of interest to the health outcome. This delivers area-level risk estimates, which allow a fuller adjustment for confounding than traditional areal studies. The methodology is illustrated by using simulations and a case study investigating the risk of lung cancer mortality associated with nitrogen dioxide in England (UK).
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DOI：
10.1198/016214504000001187
发表时间：
2004-09-01
影响因子：
3.7
作者：
Imai, K;van Dyk, DA
通讯作者：
van Dyk, DA
影响因子：
7.7
作者：
Greenland, S
通讯作者：
Greenland, S
DOI：
10.1111/j.1467-9531.2010.01226.x
发表时间：
2010-01-01
期刊：
SOCIOLOGICAL METHODOLOGY, VOL 40
影响因子：
--
作者：
An, Weihua
通讯作者：
An, Weihua
影响因子：
10.4
作者：
Hamra GB;Laden F;Cohen AJ;Raaschou-Nielsen O;Brauer M;Loomis D
通讯作者：
Loomis D
影响因子：
24.3
作者：
Faustini, Annunziata;Rapp, Regula;Forastiere, Francesco
通讯作者：
Forastiere, Francesco