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Estimating benchmark exposure for air particulate matter using latent class models.

Alfred K Mbah1, Ibrahim Hamisu, Eknath Naik

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Risk Analysis : an Official Publication of the Society for Risk Analysis
|August 2, 2014
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Summary

This study introduces a novel latent class modeling approach for benchmark exposure (BME) calculations involving multiple health outcomes. The method effectively reduces uncertainty and addresses the multiple comparisons problem in risk estimation.

Keywords:
Benchmark exposurebootstrapinfant morbiditylatent classparticulate matter

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Area of Science:

  • Environmental Health Sciences
  • Biostatistics
  • Epidemiology

Background:

  • Estimating benchmark exposure (BME) with multiple dichotomous health outcomes presents challenges, including the multiple comparisons problem.
  • Existing methods often involve fitting separate regression models for each outcome, leading to potential biases in reference exposure determination.
  • There is a need for integrated approaches that can handle multiple outcomes simultaneously to provide more robust risk estimations.

Purpose of the Study:

  • To develop and apply a novel methodology for benchmark exposure (BME) calculations when multiple dichotomous outcome variables are present.
  • To utilize latent class modeling to combine multiple outcomes into distinct risk classes for comparative analysis.
  • To address the multiple comparisons problem inherent in traditional risk estimation methods for environmental exposures.

Main Methods:

  • Latent class modeling was employed to categorize individuals into high-risk and low-risk classes based on multiple outcome variables.
  • Benchmark exposure (BME) levels were calculated by comparing the identified risk classes.
  • Bootstrap methods were used to estimate reference exposure levels and reduce uncertainty in the BME calculations.

Main Results:

  • The latent class model successfully combined several dichotomous outcomes into two distinct classes: a high-risk class and a low-risk class.
  • Separate BME results were generated for both extra risk and additional risk, providing nuanced insights into exposure-response relationships.
  • The bootstrap approach effectively estimated reference exposure levels, enhancing the reliability of the BME values.

Conclusions:

  • Latent class modeling offers an advantageous approach for benchmark exposure (BME) calculations involving multiple health outcomes, effectively managing the multiple comparisons problem.
  • The developed methodology provides a robust framework for environmental risk assessment, applicable to identifying unmeasured class membership (e.g., morbidity) in populations.
  • Application to infant data demonstrated the utility of the approach in characterizing risks associated with factors like low birth weight, preterm birth, and small for gestational age.