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Shao Y, Hwang J, MacLehose RF, Alexander BH, Mandel JH, Raynor PC, Ramachandran G. Reconstructing Historical Exposures to Respirable Dust and Respirable Silica in the Taconite Mining Industry for 1955-2010. Ann Work Expo Health 2021; 66:459-471. [PMID: 34864842 DOI: 10.1093/annweh/wxab099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 09/29/2021] [Accepted: 10/21/2021] [Indexed: 11/12/2022] Open
Abstract
The goal of this study was to reconstruct the historical respirable silica (RS) and respirable dust (RD) exposures of workers in the Minnesota taconite industry from 1955 to 2010 as part of several epidemiological studies for assessing the association between exposure to components of taconite dusts and the development of respiratory diseases. A job-exposure matrix (JEM) was developed that uses 9127 RS and 19 391 RD occupational hygiene historical measurements. Historical RS and RD data were extracted from several sources and were grouped into seven mines and then into eight departments [Concentrating, Crushing, Janitor, Mining, Office/control room, Pelletizing, Shop (mobile), and Shop (stationary)]. Within each department, we applied a two-level random-intercept regression model which assumes that the natural log of Y (RD or RS concentration) changes over time at a constant rate. Among all predicted RD and RS values, we found that larger RD values were located in the following departments: Crushing, Concentrating, Pelletizing, and Shop (mobile). Larger RS values were located only in either Crushing or Shop (mobile). The annual rates of change for historical RD and RS exposures were between -3.3 and 3.2%. The silica percentage in the dust varied by mine/department with the highest value of 29.3% in Mine F (Crushing) and the lowest value of 2.1% in Mine B (Pelletizing). The predicted historical RD and RS arithmetic mean exposures ranged between <0.075 and 3.14 mg m-3, and between <0.005 and 0.36 mg m-3, respectively. The result of this study is a JEM by mine, department, and year for RD and RS for epidemiological studies.
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Affiliation(s)
- Yuan Shao
- Division of Environmental Health Sciences, University of Minnesota, Minneapolis, MN, USA.,Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Jooyeon Hwang
- Department of Occupational and Environmental Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Richard F MacLehose
- Division of Epidemiology & Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Bruce H Alexander
- Division of Environmental Health Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Jeffrey H Mandel
- Division of Environmental Health Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Peter C Raynor
- Division of Environmental Health Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Gurumurthy Ramachandran
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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Shao Y, MacLehose RF, Lin L, Hwang J, Alexander BH, Mandel JH, Ramachandran G. A Bayesian Approach for Determining the Relationship Between Various Elongate Mineral Particles (EMPs) Definitions. Ann Work Expo Health 2020; 64:993-1006. [PMID: 33196824 DOI: 10.1093/annweh/wxaa074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 04/12/2020] [Accepted: 06/25/2020] [Indexed: 11/13/2022] Open
Abstract
A variety of dimensions (lengths and widths) of elongate mineral particles (EMPs) have been proposed as being related to health effects. In this paper, we develop a mathematical approach for deriving numerical conversion factors (CFs) between these EMP exposure metrics and applied it to the Minnesota Taconite Health Worker study which contains 196 different job exposure groups (28 similar exposure groups times 7 taconite mines). This approach comprises four steps: for each group (i) obtain EMP dimension information using ISO-TEM 10312/13794 analysis; (ii) use bivariate lognormal distribution to characterize overall EMP size distribution; (iii) use a Bayesian approach to facilitate the formation of the bivariate lognormal distribution; (iv) derive conversion factors between any pair of EMP definitions. The final CFs allow the creation of job exposure matrices (JEMs) for alternative EMP metrics using existing EMP exposures already characterized according to the National Institute of Occupational Safety and Health (NIOSH)-defined EMP exposure metric (length >5 µm with an aspect ratio ≥3.0). The relationships between the NIOSH EMP and other EMP definitions provide the basis of classification of workers into JEMs based on alternate definitions of EMP for epidemiological studies of mesothelioma, lung cancer, and non-malignant respiratory disease.
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Affiliation(s)
- Yuan Shao
- Division of Environmental Health Sciences, University of Minnesota, Minneapolis, MN, USA
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Richard F MacLehose
- Division of Epidemiology & Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Lifeng Lin
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Jooyeon Hwang
- Department of Occupational and Environmental Health, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Bruce H Alexander
- Division of Environmental Health Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Jeffrey H Mandel
- Division of Environmental Health Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Gurumurthy Ramachandran
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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Retrospective Exposure Assessment Methods Used in Occupational Human Health Risk Assessment: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17176190. [PMID: 32858967 PMCID: PMC7504303 DOI: 10.3390/ijerph17176190] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 08/24/2020] [Accepted: 08/24/2020] [Indexed: 01/02/2023]
Abstract
As part of the assessment and management of chemical risk and occupational hygiene, retrospective exposure assessment (REA) to chemical agents can be defined as the estimate of exposure associated with a person's work history. The fundamental problem underlying the reconstruction of the exposure is that of transforming this type of information in quantitative terms to obtain an accurate estimate. REA can follow various approaches, some of which are technically complicated and both time and resource consuming. The aim of this systematic review is to present the techniques mainly used for occupational REA. In order to carry out this evaluation, a systematic review of the scientific literature was conducted. Forty-four studies were identified (published from 2010 to date) and analyzed. In exposure reconstruction studies, quantitative approaches should be preferable, especially when estimates will be used in the context of health impact assessment or epidemiology, although it is important to stress how, ideally, the experimental data available for the considered scenario should be used whenever possible as the main starting information base for further processing. To date, there is no single approach capable of providing an accurate estimate of exposure for each reasonably foreseeable condition and situation and the best approach generally depends on the level of information available for the specific case. The use of a combination of different reconstruction techniques can, therefore, represent a powerful tool for weighting and integrating data obtained through qualitative and quantitative approaches, in order to obtain the best possible estimate.
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