Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster session 05

1633P - The correlation between the geographical distribution of asbestos-exposed workplaces and the increased risk of developing malignant mesothelioma, lung cancer, laryngeal cancer, and ovarian cancer: A nationwide population-based analysis in Japan

Date

10 Sep 2022

Session

Poster session 05

Topics

Population Risk Factor;  Cancer Registries

Tumour Site

Ovarian Cancer;  Mesothelioma;  Head and Neck Cancers

Presenters

Ayumi Saito

Citation

Annals of Oncology (2022) 33 (suppl_7): S743-S749. 10.1016/annonc/annonc1076

Authors

A. Saito1, H. Charvat2, T. Shimoi1, T. Matsuda3, K. Yonemori1

Author affiliations

  • 1 Department Of Medical Oncology, National Cancer Center Hospital, 1040045 - Tokyo/JP
  • 2 Faculty Of International Liberal Arts, Juntendo University, 113-8421 - Tokyo/JP
  • 3 Center For Cancer Registries, Center For Cancer Control And Information Services, National Cancer Center Research Institiute - Tsukiji Campus, 104-0045 - Chuo-ku/JP

Resources

Login to get immediate access to this content.

If you do not have an ESMO account, please create one for free.

Abstract 1633P

Background

Asbestos exposure causes some malignancies. Several studies and financial aids for mesothelioma patients are available but are limited for other malignancies. This study aimed to determine the distribution of incidence and the association of malignancies with asbestos exposure.

Methods

Data on mesothelioma, lung cancer, ovarian cancer, and laryngeal cancer from 2011 to 2017 were acquired from the national cancer registry database in Japan. The density of asbestos-exposed workplaces was determined from public data from 2005 to 2020. Variation in age-standardized incidence rates (ASR) between prefectures was assessed by funnel plots. An adjusted Mixed-effect Poisson model was used to study the association between the density of asbestos-exposed workplaces and cancer incidence.

Results

The study included 9,743 patients with pleural mesothelioma, 1,607 patients with non-pleural mesothelioma, 765,484 patients with lung cancer, and 35,187 patients with laryngeal cancer, and 75,166 patients with ovarian cancer. A total of 12,148 asbestos-exposed workplaces were identified. Prefectures with more asbestos-exposed workplaces had significantly higher incidence rates for pleural mesothelioma (incidence rate ratio [IRR] 1.85, p < 0.05), non-pleural mesothelioma among males only (IRR 1.34,p < 0.05), lung cancer (IRR 1.14, p < 0.005), and laryngeal cancer (IRR 1.12, p < 0.05). The incidence of non-pleural mesothelioma in women tended to be higher in prefectures with many asbestos-exposed workplaces.(IRR 1.25, p = 0.15).

Conclusions

This study revealed that prefectures with more asbestos-exposed workplaces had a higher incidence rate of mesothelioma, lung cancer, and laryngeal cancer. This large population-based analysis suggested a strong association between asbestos-exposed workplaces and the incidence of malignancies. Moreover, a higher incidence of malignancies was also observed among women with less occupational exposure. This indicated that non-occupational exposures, including familial and neighborhood factors, contributed to the increased risk of malignancies.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.