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Estimating Benchmark Exposure for Air Particulate Matter Using Latent Class Models
Author(s) -
Mbah Alfred K.,
Hamisu Ibrahim,
Naik Eknath,
Salihu Hamisu M.
Publication year - 2014
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.12256
Subject(s) - benchmark (surveying) , statistics , latent class model , latent variable , class (philosophy) , regression analysis , estimation , outcome (game theory) , econometrics , mathematics , computer science , artificial intelligence , engineering , geography , geodesy , systems engineering , mathematical economics
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.