{"title":"Using life expectancy as a risk assessment metric: The case of respirable crystalline silica","authors":"Andrey A. Korchevskiy , Arseniy Korchevskiy","doi":"10.1016/j.comtox.2023.100285","DOIUrl":null,"url":null,"abstract":"<div><p>The change in age-related mortality patterns is an important characteristic of the population that can be used as a metric of risk by comparing exposed and non-exposed populations.</p><p>In this paper, the mortality parameters were predicted for populations exposed to crystalline silica, a proven lung carcinogen.</p><p>Seven hazard functions were tested for a dose–response relationship between lung cancer and characteristics of exposure. Life tables were calculated, along with parameters of the Gompertz-Makeham model for the force of mortality.</p><p>It was demonstrated, in particular, that exposure to crystalline silica in the range from 0.03 to 0.3 mg/m<sup>3</sup> for 40 years starting at age 20 causes a predicted drop in average life expectancy in the range of from 0.15 to 1.38 years.</p><p>It was demonstrated that the lost life expectancy linearly correlates with relative risk (R = 0.995, R<sup>2</sup><span> = 0.989, p< 0.00001). The probability of the life expectancy increasing while relative risk decreases was as low as 0.01.</span></p><p><span>It was found that exponential parameter α of the Gompertz-Makeham equation increases with crystalline silica exposure, while the two linear parameters A and R (which are negatively correlated between each other) increase or decrease with exposure depending on the duration and onset age. Modal age of death in the cohort decreases with cumulative exposure with R = -0.977, R</span><sup>2</sup> = 0.954, p < 0.0001.</p><p>Based on several different approaches, it was suggested that the threshold of cumulative crystalline silica exposure concentration causing statistically significant change in the cohort life tables can be found in the range from 1.81 to 2.50 mg/m<sup>3</sup>-years. The change of average age of death in exposed male population does not exceed 1% below cumulative exposure of 3.5 mg/m<sup>3</sup>-years, and does not exceed 5% at cumulative exposure less than 9.8 mg/m<sup>3</sup>-years. It shows that no significant acceleration of death rate with age is happening even at the high levels of exposure to crystalline silica.</p><p>The study demonstrated the value and advantages of the use of life expectancy and other lifetable characteristics as a tool for quantitative risk assessment.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111323000269","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The change in age-related mortality patterns is an important characteristic of the population that can be used as a metric of risk by comparing exposed and non-exposed populations.
In this paper, the mortality parameters were predicted for populations exposed to crystalline silica, a proven lung carcinogen.
Seven hazard functions were tested for a dose–response relationship between lung cancer and characteristics of exposure. Life tables were calculated, along with parameters of the Gompertz-Makeham model for the force of mortality.
It was demonstrated, in particular, that exposure to crystalline silica in the range from 0.03 to 0.3 mg/m3 for 40 years starting at age 20 causes a predicted drop in average life expectancy in the range of from 0.15 to 1.38 years.
It was demonstrated that the lost life expectancy linearly correlates with relative risk (R = 0.995, R2 = 0.989, p< 0.00001). The probability of the life expectancy increasing while relative risk decreases was as low as 0.01.
It was found that exponential parameter α of the Gompertz-Makeham equation increases with crystalline silica exposure, while the two linear parameters A and R (which are negatively correlated between each other) increase or decrease with exposure depending on the duration and onset age. Modal age of death in the cohort decreases with cumulative exposure with R = -0.977, R2 = 0.954, p < 0.0001.
Based on several different approaches, it was suggested that the threshold of cumulative crystalline silica exposure concentration causing statistically significant change in the cohort life tables can be found in the range from 1.81 to 2.50 mg/m3-years. The change of average age of death in exposed male population does not exceed 1% below cumulative exposure of 3.5 mg/m3-years, and does not exceed 5% at cumulative exposure less than 9.8 mg/m3-years. It shows that no significant acceleration of death rate with age is happening even at the high levels of exposure to crystalline silica.
The study demonstrated the value and advantages of the use of life expectancy and other lifetable characteristics as a tool for quantitative risk assessment.
期刊介绍:
Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs