Software for Apportionment of Asbestos-Related Mesotheliomas

Our plain-English summary of Software for Apportionment of Asbestos-Related Mesotheliomas, by R.M. Ross (Canadian Respiratory Journal, 2016).

The study

• Author: R.M. Ross
• Journal: Canadian Respiratory Journal
• Published: 2016;2016:5340676
• Type of article: Review
• Read it: PubMed · Publisher · Free full text

Abstract

Patients with an asbestos-related mesothelioma may be legally entitled to financial compensation. In this context, a physician may be called upon to apportion the contribution of an asbestos containing product or facility where there was asbestos exposure in the development of that individual's mesothelioma. This task is mathematically not simple. It is a complex function of each and the entire individual's above-background asbestos exposures. Factors to be considered for each of these exposures are the amount of exposure to mesotheliogenic fibers, each of the asbestos containing products' potency to cause mesothelioma, and the time period when the exposures occurred relative to when the mesothelioma was diagnosed. In this paper, the known factors related to asbestos-related mesothelioma risk are briefly reviewed and the software that is downloadable and fully functional in a Windows® environment is also provided. This software allows for rapid assessment of relative contributions and deals with the somewhat tedious mathematical calculations. With this software and a reasonable occupational history, if it is decided that the mesothelioma was due to above-background asbestos exposure, the contribution of an asbestos containing product or a time period of asbestos exposure can be apportioned.

In plain English

This review article by one author briefly reviewed published research on factors thought to affect the risk of asbestos-related mesothelioma, including fiber length, time since exposure, dose and fiber type. It also provided downloadable Windows software, called MESOContribution, that calculates how much each asbestos product, workplace or time period contributed to one person's mesothelioma. The article reported no new patient data. The software assumed that every exposure above background contributed in proportion to its 'mesotheliogenic potential.' For each entry, it multiplied exposure concentration by a potency value chosen by the user and by the time exposed, weighted by the square of the years from exposure (plus a 10-year lag) to diagnosis. In a hypothetical example of a person diagnosed in 2013, 'Product A' contributed about 44% of the apportionment. The author stated that the program does not decide whether a mesothelioma is asbestos-related, and that results are only as accurate as the values entered. The author concluded the software can be used to apportion contributions when a mesothelioma is asbestos-related. The author reported acting as a medical expert in asbestos-related litigation.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Review article with a description of calculation software and a hypothetical worked example; no original patient data
• Who was studied: No study population. The software was illustrated with one hypothetical person found to have an asbestos-related mesothelioma in 2013.
• Exposure: Any above-background asbestos exposure, entered by the user as months of exposure, relative concentration and relative potency. The article discussed commercial chrysotile (usually containing small amounts of amphibole such as tremolite) and amphiboles (crocidolite, amosite). As an example, it suggested an amphibole-to-commercial-chrysotile potency ratio of about 50 to 1.
• Main results:
  • The software's calculation: each entry's risk equals exposure concentration times potency value times months/12, times (year of diagnosis minus (entry year plus 10)) squared. Each entry's percentage is its share of the sum of all entries.
  • The program assumes that an exposure of similar type and dose occurring twice as far in the past (lagged by 10 years) has a fourfold greater effect on mesothelioma risk.
  • The program assumes a linear relationship between respirable exposure dose and mesothelioma risk.
  • The user, not the program, supplies the relative potencies of the asbestos types. The author gave about 50 to 1 (amphibole to commercial chrysotile) as an example estimate. Mixed exposure (about 90% chrysotile, 10% amphibole) was then given an average potency of about 6.
  • The author stated that only relative, not absolute, exposure doses and potencies are needed, because absolute values cancel out mathematically.
  • In the hypothetical example (diagnosis 2013), Product A accounted for 43.70% of the calculated total and miscellaneous other products for 56.30%. The text rounds the Product A figure to about 44%.
• The authors' conclusion: The author concluded that, when an individual has an asbestos-related mesothelioma, the software can be used to apportion the contribution of a particular asbestos-containing product, workplace or time period of exposure.
• Limitations the authors state:
  • The program does not determine whether a mesothelioma is asbestos-related.
  • The program does not determine actual exposures or correct potencies; the operator chooses the input values, and results are only as accurate as that input.
  • Other factors, such as a threshold concentration, may later be found to play a role.
  • A person's memory of past exposures can significantly affect the results.

Topics

The National Library of Medicine indexes this study under subjects our site covers: Lung cancer, Mesothelioma.

Medical terms in this study, explained in our Lexicon: Mesothelioma, Asbestos Exposure, Occupational History.

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