Asbestos Exposure - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Asbestos Exposure

The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform

Hendrik Koffijberg – 1st expert on this subject based on the ideXlab platform

  • lung cancer risk at low cumulative Asbestos Exposure meta regression of the Exposure response relationship
    Cancer Causes & Control, 2013
    Co-Authors: Hendrik Koffijberg, Roel Vermeulen, Virissa Lenters, Lutzen Portengen, Karel G M Moons, Dick Heederik

    Abstract:

    Purpose
    Existing estimated lung cancer risks per unit of Asbestos Exposure are mainly based on, and applicable to, high Exposure levels. To assess the risk at low cumulative Asbestos Exposure, we provide new evidence by fitting flexible meta-regression models, a notably new and more robust method.

  • Lung cancer risk at low cumulative Asbestos Exposure: meta-regression of the Exposure–response relationship
    Cancer Causes & Control, 2013
    Co-Authors: Hendrik Koffijberg, Virissa Lenters, Lutzen Portengen, Karel G M Moons, Dick Heederik, Roel C. H. Vermeulen

    Abstract:

    Purpose Existing estimated lung cancer risks per unit of Asbestos Exposure are mainly based on, and applicable to, high Exposure levels. To assess the risk at low cumulative Asbestos Exposure, we provide new evidence by fitting flexible meta-regression models, a notably new and more robust method. Methods Studies were selected if lung cancer risk per cumulative Asbestos Exposure in at least two Exposure categories was reported. From these studies ( n  = 19), we extracted 104 risk estimates over a cumulative Exposure range of 0.11–4,710 f-y/ml. We fitted linear and natural spline meta-regression models to these risk estimates. A natural spline allows risks to vary nonlinearly with Exposure, such that estimates at low Exposure are less affected by estimates in the upper Exposure categories. Associated relative risks (RRs) were calculated for several low cumulative Asbestos Exposures. Results A natural spline model fitted our data best. With this model, the relative lung cancer risk for cumulative Exposure levels of 4 and 40 f-y/ml was estimated between 1.013 and 1.027, and 1.13 and 1.30, respectively. After stratification by fiber type, a non-significant three- to fourfold difference in RRs between chrysotile and amphibole fibers was found for Exposures below 40 f-y/ml. Fiber-type-specific risk estimates were strongly influenced by a few studies. Conclusions The natural spline regression model indicates that at lower Asbestos Exposure levels, the increase in RR of lung cancer due to Asbestos Exposure may be larger than expected from previous meta-analyses. Observed potency differences between different fiber types are lower than the generally held consensus. Low-exposed industrial or population-based cohorts with quantitative estimates of Asbestos Exposure a required to substantiate the risk estimates at low Exposure levels from our new, flexible meta-regression.

Roel Vermeulen – 2nd expert on this subject based on the ideXlab platform

  • peritoneal mesothelioma and Asbestos Exposure a population based case control study in lombardy italy
    Occupational and Environmental Medicine, 2019
    Co-Authors: Dario Consonni, Sara De Matteis, Dario Mirabelli, Cristina Calvi, Maria Teresa Landi, Neil E Caporaso, Susan Peters, Roel Vermeulen

    Abstract:

    Objectives Asbestos is the main risk factor for peritoneal mesothelioma (PeM). However, due to its rarity, PeM has rarely been investigated in community-based studies. We examined the association between Asbestos Exposure and PeM risk in a general population in Lombardy, Italy. Methods From the regional mesothelioma registry, we selected PeM cases diagnosed in 2000–2015. Population controls (matched by area, gender and age) came from two case–control studies in Lombardy on lung cancer (2002–2004) and pleural mesothelioma (2014). Assessment of Exposure to Asbestos was performed through a quantitative job-Exposure matrix (SYN-JEM) and expert evaluation based on a standardised questionnaire. We calculated period-specific and gender-specific OR and 90% CI using conditional logistic regression adjusted for age, province of residence and education. Results We selected 68 cases and 2116 controls (2000–2007) and 159 cases and 205 controls (2008–2015). The ORs for ever Asbestos Exposure (expert-based, 2008–2015 only) were 5.78 (90% CI 3.03 to 11.0) in men and 8.00 (2.56 to 25.0) in women; the ORs for definite occupational Exposure were 12.3 (5.62 to 26.7) in men and 14.3 (3.16 to 65.0) in women. The ORs for ever versus never occupational Asbestos Exposure based on SYN-JEM (both periods) were 2.05 (90% CI 1.39 to 3.01) in men and 1.62 (0.79 to 3.27) in women. In men, clear positive associations were found for duration, cumulative Exposure (OR 1.33 (1.19 to 1.48) per fibres/mL-years) and latency. Conclusions Using two different methods of Exposure assessment we provided evidence of a clear association between Asbestos Exposure and PeM risk in the general population.

  • lung cancer risk at low cumulative Asbestos Exposure meta regression of the Exposure response relationship
    Cancer Causes & Control, 2013
    Co-Authors: Hendrik Koffijberg, Roel Vermeulen, Virissa Lenters, Lutzen Portengen, Karel G M Moons, Dick Heederik

    Abstract:

    Purpose
    Existing estimated lung cancer risks per unit of Asbestos Exposure are mainly based on, and applicable to, high Exposure levels. To assess the risk at low cumulative Asbestos Exposure, we provide new evidence by fitting flexible meta-regression models, a notably new and more robust method.

Dick Heederik – 3rd expert on this subject based on the ideXlab platform

  • lung cancer risk at low cumulative Asbestos Exposure meta regression of the Exposure response relationship
    Cancer Causes & Control, 2013
    Co-Authors: Hendrik Koffijberg, Roel Vermeulen, Virissa Lenters, Lutzen Portengen, Karel G M Moons, Dick Heederik

    Abstract:

    Purpose
    Existing estimated lung cancer risks per unit of Asbestos Exposure are mainly based on, and applicable to, high Exposure levels. To assess the risk at low cumulative Asbestos Exposure, we provide new evidence by fitting flexible meta-regression models, a notably new and more robust method.

  • Lung cancer risk at low cumulative Asbestos Exposure: meta-regression of the Exposure–response relationship
    Cancer Causes & Control, 2013
    Co-Authors: Hendrik Koffijberg, Virissa Lenters, Lutzen Portengen, Karel G M Moons, Dick Heederik, Roel C. H. Vermeulen

    Abstract:

    Purpose Existing estimated lung cancer risks per unit of Asbestos Exposure are mainly based on, and applicable to, high Exposure levels. To assess the risk at low cumulative Asbestos Exposure, we provide new evidence by fitting flexible meta-regression models, a notably new and more robust method. Methods Studies were selected if lung cancer risk per cumulative Asbestos Exposure in at least two Exposure categories was reported. From these studies ( n  = 19), we extracted 104 risk estimates over a cumulative Exposure range of 0.11–4,710 f-y/ml. We fitted linear and natural spline meta-regression models to these risk estimates. A natural spline allows risks to vary nonlinearly with Exposure, such that estimates at low Exposure are less affected by estimates in the upper Exposure categories. Associated relative risks (RRs) were calculated for several low cumulative Asbestos Exposures. Results A natural spline model fitted our data best. With this model, the relative lung cancer risk for cumulative Exposure levels of 4 and 40 f-y/ml was estimated between 1.013 and 1.027, and 1.13 and 1.30, respectively. After stratification by fiber type, a non-significant three- to fourfold difference in RRs between chrysotile and amphibole fibers was found for Exposures below 40 f-y/ml. Fiber-type-specific risk estimates were strongly influenced by a few studies. Conclusions The natural spline regression model indicates that at lower Asbestos Exposure levels, the increase in RR of lung cancer due to Asbestos Exposure may be larger than expected from previous meta-analyses. Observed potency differences between different fiber types are lower than the generally held consensus. Low-exposed industrial or population-based cohorts with quantitative estimates of Asbestos Exposure a required to substantiate the risk estimates at low Exposure levels from our new, flexible meta-regression.