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Estimating Benchmark Exposure for Air Particulate Matter Using Latent Class Models

机译：使用潜在类模型估算空气颗粒物的基准暴露量

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摘要

We performed benchmark exposure (BME) calculations for particulate matter when multiple dichotomous outcome variables are involved using latent class modeling techniques and generated separate results for both the extra risk and additional risk. The use of latent class models in this study is advantageous because it combined several outcomes into just two classes (namely, a high-risk class and a low-risk class) and compared these two classes to obtain the BME levels. This novel approach addresses a key problem in risk estimation-namely, the multiple comparisons problem, where separate regression models are fitted for each outcome variable and the reference exposure will rely on the results of the best-fitting model. Because of the complex nature of the estimation process, the bootstrap approach was used to estimate the reference exposure level, thereby reducing uncertainty in the obtained values. The methodology developed in this article was applied to environmental data by identifying unmeasured class membership (e.g., morbidity vs. no morbidity class) among infants in utero using observed characteristics that included low birth weight, preterm birth, and small for gestational age.
机译：当使用潜在类建模技术涉及多个二分结果变量时，我们对颗粒物进行了基准暴露（BME）计算，并针对额外风险和额外风险分别生成了结果。在此研究中使用潜在类别模型是有利的，因为它将几种结果仅组合到两个类别（即高风险类别和低风险类别）中，并比较了这两个类别以获得BME水平。这种新颖的方法解决了风险估计中的一个关键问题，即多重比较问题，其中对每个结果变量都拟合了单独的回归模型，而参考暴露将依赖于最佳拟合模型的结果。由于估算过程的复杂性，自举方法用于估算参考暴露水平，从而减少了获得值的不确定性。本文开发的方法通过使用观察到的特征（包括低出生体重，早产和胎龄小）来识别子宫内婴儿的未测分类成员（例如发病率与无发病率类别），从而将其应用于环境数据。

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  • 来源
    《Risk analysis》 |2014年第11期|2053-2062|共10页
  • 作者

    Alfred K. Mbah; Ibrahim Hamisu; Eknath Naik; Hamisu M. Salihu;

  • 作者单位

    Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd, MDC-56, Tampa, FL 33612, USA;

    Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (UC Berkeley), CA, USA;

    Department of Global Health, College of Public Health, University of South Florida, Tampa, FL, USA;

    Department of Epidemiology and Biostatistics, University of South Florida, Tampa, FL, USA,Department of Obstetrics and Gynecology, University of South Florida, Tampa, FL, USA;

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  • 正文语种 eng
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  • 关键词

    Benchmark exposure; bootstrap; infant morbidity; latent class; particulate matter;

    机译：基准曝光;引导程序婴儿发病率潜类颗粒物;

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