Estimating Benchmark Exposure for Air Particulate Matter Using Latent Class Models

Authors

  • Alfred K. Mbah,

    Corresponding author
    1. Department of Epidemiology and Biostatistics, University of South Florida, Tampa, FL, USA
    • Address correspondence to Alfred K. Mbah, Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd, MDC-56, Tampa, FL 33612, USA; tel: 813-974-1118; ambah@health.usf.edu.

    Search for more papers by this author
  • Ibrahim Hamisu,

    1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (UC Berkeley), CA, USA
    Search for more papers by this author
  • Eknath Naik,

    1. Department of Global Health, College of Public Health, University of South Florida, Tampa, FL, USA
    Search for more papers by this author
  • Hamisu M. Salihu

    1. Department of Epidemiology and Biostatistics, University of South Florida, Tampa, FL, USA
    2. Department of Obstetrics and Gynecology, University of South Florida, Tampa, FL, USA
    Search for more papers by this author

Abstract

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.

Ancillary