Mining Contract: Integration of RCMD and RCS Physicochemistry and Toxicity Outcomes in an Occupational Risk Assessment Model

Contract # 75D30121C12182
Start Date 9/1/2021
Research Concept

Predicting which miners will eventually develop lung diseases over time and delineating risk-factors in the industry is still unknown. In this project, researchers will use an interdisciplinary approach, including mining engineering, environmental chemistry, environmental toxicology, molecular epidemiology, and machine learning expertise, to investigate respirable coal mine dust (RCMD) and respirable silica dust (RCS) dust toxicity based on particle characteristics and exposure dose, and establish a model with risk factors contributing to the lung-term health outcomes in coal miners.

Contract Status & Impact

This contract is ongoing. For more information on this contract, send a request to mining@cdc.gov.

Despite several studies on RCMD and RCS exposure and health risks, data are still too meager to draw conclusive conclusions on population lung disease causes. Furthermore, there is currently no established risk-model for occupational dust exposure and subsequent lung-disease risk. Establishment of a valid model could help identify high-risk mines for early intervention.

Investigators will develop the risk assessment model using an innovative machine learning (ML) approach to assess the susceptibility of coal miners to development of coal workers' pneumoconiosis (CWP). To achieve the overarching goal, researchers will pursue the following specific aims:

  1. Conduct characterization studies (i.e., size, shape, surface area, mineralogy, elemental components) on RCMD and RCS sample
  2. Investigate RCMD and RCS toxicity using simulated lung fluid and in vitro toxicity comparisons of respirable dust
  3. Establish a comprehensive mine-, dust-, and health- specific database and identify potential risk factors contributing/leading to CWP development
  4. Integrate risk factors associated with CWP in a risk assessment model using innovative machine learning techniques
Page last reviewed: 1/14/2022 Page last updated: 1/14/2022