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Open Access Publications from the University of California

Scholarly Works (4 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2018)

In industrial hygiene, prediction of a worker's exposure to chemical concentrations at the workplace is important for exposure management and prevention. The objective of this dissertation is to consider and address challenges in the statistical analyses of exposure data

in industrial hygiene. We outline a flexible Bayesian frameworks for parameter inference and exposure prediction. In particular, we will focus on two applications of the Bayesian approach on exposure data. The rst application is spatial interpolation of chemical concentrations at new locations when measurements are available from coastlines, as is the case in coastal clean-up operations in oil spills. We present novel yet simple methodology for analyzing spatial data that is observed over a coastline. We demonstrate four dierent models using two different representations of the coast. The four models were demonstrated on simulated data and two of them were also demonstrated on a dataset from the GuLF STUDY. Our contribution here is to oer practicing hygienists and exposure assessors with a simple and easy method to implement Bayesian hierarchical models for analyzing and interpolating coastal chemical concentrations.The second application is inference and prediction of chemical concentrations at the workplace using state space models. Exposure assessment models are deterministic models that are usually derived from physical-chemical laws that explain the workplace under theoretically ideal conditions. We propose Bayesian parametric and nonparametric approaches for modeling exposure data in industrial hygiene using a state space model framework which combines information from observations, physical processes and prior knowledge. Posterior inference is obtained via easy implementable Markov chain Monte Carlo (MCMC) algorithms. The performance of the dierent methods will be studied on computer-simulated and controlled laboratory-generated data. We will consider three commonly used occupational exposure physical models varying in complexity.

    Cover page: Parametric and Non-parametric Bayesian Modeling of Spatio-temporal Exposure Data in Industrial Hygiene
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2018)

    Introduction

    We forecast the prevalence of preclinical and clinical Alzheimer's disease (AD) and evaluated potential impacts of primary and secondary preventions in the United States.

    Methods

    We used a multistate model incorporating biomarkers for preclinical AD with US population projections.

    Results

    Approximately 6.08 million Americans had either clinical AD or mild cognitive impairment due to AD in 2017 and that will grow to 15.0 million by 2060. In 2017, 46.7 million Americans had preclinical AD (amyloidosis, neurodegeneration, or both), although many may not progress to clinical disease during their lifetimes. Primary and secondary preventions have differential impact on future disease burden.

    Discussion

    Because large numbers of persons are living with preclinical AD, our results underscore the need for secondary preventions for persons with existing AD brain pathology who are likely to develop clinical disease during their lifetimes as well as primary preventions for persons without preclinical disease.
      Cover page: Forecasting the prevalence of preclinical and clinical Alzheimer's disease in the United StatesCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      Research Reports (2018)
      Statistical interpolation of chemical concentrations at new locations is an important step in assessinga worker’s exposure level. When measurements are available from coastlines, as is the case incoastal clean-up operations in oil spills, one may need a mechanism to carry out spatial interpolationat new locations along the coast. In this article, we present a simple model for analyzing spatial datathat is observed over a coastline. We demonstrate four different models using two different representationsof the coast using curves. The four models were demonstrated on simulated data andone of them was also demonstrated on a dataset from the GuLF STUDY (Gulf Long-term Follow-upStudy). Our contribution here is to offer practicing hygienists and exposure assessors with a simpleand easy method to implement Bayesian hierarchical models for analyzing and interpolating coastalchemical concentrations.
        Cover page: Coastline Kriging: A Bayesian Approach
        • Article
        • Peer Reviewed
        Research Reports (2018)
          Cover page: Bayesian State Space Modeling of PhysicalProcesses in Industrial Hygiene
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